Overview

Dataset statistics

Number of variables43
Number of observations33719
Missing cells267884
Missing cells (%)18.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory344.0 B

Variable types

Numeric23
Categorical20

Alerts

pl_name has a high cardinality: 5197 distinct values High cardinality
hostname has a high cardinality: 3888 distinct values High cardinality
disc_facility has a high cardinality: 66 distinct values High cardinality
pl_refname has a high cardinality: 1861 distinct values High cardinality
st_refname has a high cardinality: 1701 distinct values High cardinality
st_spectype has a high cardinality: 225 distinct values High cardinality
sy_refname has a high cardinality: 149 distinct values High cardinality
rastr has a high cardinality: 3885 distinct values High cardinality
decstr has a high cardinality: 3894 distinct values High cardinality
rowupdate has a high cardinality: 362 distinct values High cardinality
pl_pubdate has a high cardinality: 265 distinct values High cardinality
releasedate has a high cardinality: 320 distinct values High cardinality
pl_orbper is highly correlated with disc_facility and 1 other fieldsHigh correlation
pl_orbsmax is highly correlated with disc_facility and 1 other fieldsHigh correlation
pl_rade is highly correlated with pl_radj and 2 other fieldsHigh correlation
pl_radj is highly correlated with disc_facility and 2 other fieldsHigh correlation
pl_bmasse is highly correlated with pl_bmassj and 3 other fieldsHigh correlation
pl_bmassj is highly correlated with pl_bmasse and 3 other fieldsHigh correlation
pl_orbeccen is highly correlated with disc_facility and 4 other fieldsHigh correlation
pl_insol is highly correlated with pl_bmasse and 5 other fieldsHigh correlation
pl_eqt is highly correlated with pl_radj and 5 other fieldsHigh correlation
st_teff is highly correlated with discoverymethod and 2 other fieldsHigh correlation
st_rad is highly correlated with disc_facility and 6 other fieldsHigh correlation
st_mass is highly correlated with pl_radj and 5 other fieldsHigh correlation
st_logg is highly correlated with sy_pnum and 6 other fieldsHigh correlation
sy_dist is highly correlated with discoverymethod and 5 other fieldsHigh correlation
sy_vmag is highly correlated with loc_rowid and 7 other fieldsHigh correlation
sy_kmag is highly correlated with loc_rowid and 11 other fieldsHigh correlation
sy_gaiamag is highly correlated with loc_rowid and 9 other fieldsHigh correlation
discoverymethod is highly correlated with loc_rowid and 9 other fieldsHigh correlation
disc_facility is highly correlated with loc_rowid and 17 other fieldsHigh correlation
soltype is highly correlated with disc_facility and 1 other fieldsHigh correlation
pl_bmassprov is highly correlated with loc_rowid and 7 other fieldsHigh correlation
default_flag is highly correlated with pl_orbeccenHigh correlation
ra is highly correlated with loc_rowid and 5 other fieldsHigh correlation
dec is highly correlated with loc_rowid and 7 other fieldsHigh correlation
loc_rowid is highly correlated with discoverymethod and 8 other fieldsHigh correlation
sy_snum is highly correlated with disc_facilityHigh correlation
sy_pnum is highly correlated with st_loggHigh correlation
disc_year is highly correlated with loc_rowid and 6 other fieldsHigh correlation
ttv_flag is highly correlated with disc_yearHigh correlation
pl_orbper has 2845 (8.4%) missing values Missing
pl_orbsmax has 15252 (45.2%) missing values Missing
pl_rade has 10403 (30.9%) missing values Missing
pl_radj has 23236 (68.9%) missing values Missing
pl_bmasse has 28726 (85.2%) missing values Missing
pl_bmassj has 28727 (85.2%) missing values Missing
pl_bmassprov has 28726 (85.2%) missing values Missing
pl_orbeccen has 16824 (49.9%) missing values Missing
pl_insol has 19437 (57.6%) missing values Missing
pl_eqt has 18385 (54.5%) missing values Missing
st_spectype has 31764 (94.2%) missing values Missing
st_teff has 2377 (7.0%) missing values Missing
st_rad has 2257 (6.7%) missing values Missing
st_mass has 5237 (15.5%) missing values Missing
st_met has 12431 (36.9%) missing values Missing
st_metratio has 12620 (37.4%) missing values Missing
st_logg has 5939 (17.6%) missing values Missing
sy_dist has 793 (2.4%) missing values Missing
sy_vmag has 422 (1.3%) missing values Missing
sy_kmag has 443 (1.3%) missing values Missing
sy_gaiamag has 726 (2.2%) missing values Missing
pl_orbper is highly skewed (γ1 = 175.5148307) Skewed
pl_orbsmax is highly skewed (γ1 = 68.85274754) Skewed
pl_rade is highly skewed (γ1 = 49.08722241) Skewed
pl_insol is highly skewed (γ1 = 23.8063538) Skewed
st_rad is highly skewed (γ1 = 23.0423617) Skewed
loc_rowid is uniformly distributed Uniform
loc_rowid has unique values Unique
pl_orbeccen has 13601 (40.3%) zeros Zeros
st_met has 749 (2.2%) zeros Zeros

Reproduction

Analysis started2022-11-07 19:46:41.142558
Analysis finished2022-11-07 19:48:25.936124
Duration1 minute and 44.79 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

loc_rowid
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct33719
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16860
Minimum1
Maximum33719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:26.032149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1686.9
Q18430.5
median16860
Q325289.5
95-th percentile32033.1
Maximum33719
Range33718
Interquartile range (IQR)16859

Descriptive statistics

Standard deviation9733.9812
Coefficient of variation (CV)0.5773417082
Kurtosis-1.2
Mean16860
Median Absolute Deviation (MAD)8430
Skewness0
Sum568502340
Variance94750390
MonotonicityStrictly increasing
2022-11-08T01:18:26.168455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
224911
 
< 0.1%
224891
 
< 0.1%
224881
 
< 0.1%
224871
 
< 0.1%
224861
 
< 0.1%
224851
 
< 0.1%
224841
 
< 0.1%
224831
 
< 0.1%
224821
 
< 0.1%
Other values (33709)33709
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
337191
< 0.1%
337181
< 0.1%
337171
< 0.1%
337161
< 0.1%
337151
< 0.1%
337141
< 0.1%
337131
< 0.1%
337121
< 0.1%
337111
< 0.1%
337101
< 0.1%

pl_name
Categorical

HIGH CARDINALITY

Distinct5197
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
TrES-2 b
 
32
GJ 436 b
 
21
HAT-P-7 b
 
21
HD 189733 b
 
20
Kepler-10 b
 
19
Other values (5192)
33606 

Length

Max length29
Median length28
Mean length11.69919037
Min length5

Characters and Unicode

Total characters394485
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)2.3%

Sample

1st row11 Com b
2nd row11 Com b
3rd row11 UMi b
4th row11 UMi b
5th row11 UMi b

Common Values

ValueCountFrequency (%)
TrES-2 b32
 
0.1%
GJ 436 b21
 
0.1%
HAT-P-7 b21
 
0.1%
HD 189733 b20
 
0.1%
Kepler-10 b19
 
0.1%
HD 209458 b19
 
0.1%
KOI-13 b19
 
0.1%
HAT-P-11 b18
 
0.1%
Kepler-25 c17
 
0.1%
Kepler-7 b17
 
0.1%
Other values (5187)33516
99.4%

Length

2022-11-08T01:18:26.304773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b24650
35.0%
c5890
 
8.4%
d2140
 
3.0%
hd1739
 
2.5%
e737
 
1.0%
gj284
 
0.4%
f237
 
0.3%
a142
 
0.2%
epic111
 
0.2%
hip105
 
0.1%
Other values (3983)34405
48.8%

Most occurring characters

ValueCountFrequency (%)
e52419
13.3%
36721
 
9.3%
-32321
 
8.2%
K27943
 
7.1%
r26009
 
6.6%
p25851
 
6.6%
l25812
 
6.5%
b24640
 
6.2%
122982
 
5.8%
214919
 
3.8%
Other values (53)104868
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter164197
41.6%
Decimal Number113161
28.7%
Uppercase Letter47980
 
12.2%
Space Separator36721
 
9.3%
Dash Punctuation32321
 
8.2%
Other Punctuation64
 
< 0.1%
Math Symbol41
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K27943
58.2%
H2675
 
5.6%
A2126
 
4.4%
P1910
 
4.0%
D1814
 
3.8%
T1806
 
3.8%
S1576
 
3.3%
L1242
 
2.6%
O1112
 
2.3%
W1072
 
2.2%
Other values (16)4704
 
9.8%
Lowercase Letter
ValueCountFrequency (%)
e52419
31.9%
r26009
15.8%
p25851
15.7%
l25812
15.7%
b24640
15.0%
c5965
 
3.6%
d2174
 
1.3%
o309
 
0.2%
f247
 
0.2%
a196
 
0.1%
Other values (12)575
 
0.4%
Decimal Number
ValueCountFrequency (%)
122982
20.3%
214919
13.2%
311665
10.3%
49688
8.6%
59450
8.4%
69301
8.2%
78914
 
7.9%
88893
 
7.9%
08792
 
7.8%
98557
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.62
96.9%
'2
 
3.1%
Space Separator
ValueCountFrequency (%)
36721
100.0%
Dash Punctuation
ValueCountFrequency (%)
-32321
100.0%
Math Symbol
ValueCountFrequency (%)
+41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin212177
53.8%
Common182308
46.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e52419
24.7%
K27943
13.2%
r26009
12.3%
p25851
12.2%
l25812
12.2%
b24640
11.6%
c5965
 
2.8%
H2675
 
1.3%
d2174
 
1.0%
A2126
 
1.0%
Other values (38)16563
 
7.8%
Common
ValueCountFrequency (%)
36721
20.1%
-32321
17.7%
122982
12.6%
214919
8.2%
311665
 
6.4%
49688
 
5.3%
59450
 
5.2%
69301
 
5.1%
78914
 
4.9%
88893
 
4.9%
Other values (5)17454
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII394485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e52419
13.3%
36721
 
9.3%
-32321
 
8.2%
K27943
 
7.1%
r26009
 
6.6%
p25851
 
6.6%
l25812
 
6.5%
b24640
 
6.2%
122982
 
5.8%
214919
 
3.8%
Other values (53)104868
26.6%

hostname
Categorical

HIGH CARDINALITY

Distinct3888
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
Kepler-11
 
85
Kepler-32
 
76
Kepler-186
 
75
Kepler-296
 
69
Kepler-33
 
62
Other values (3883)
33352 

Length

Max length27
Median length25
Mean length9.697737181
Min length3

Characters and Unicode

Total characters326998
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)1.3%

Sample

1st row11 Com
2nd row11 Com
3rd row11 UMi
4th row11 UMi
5th row11 UMi

Common Values

ValueCountFrequency (%)
Kepler-1185
 
0.3%
Kepler-3276
 
0.2%
Kepler-18675
 
0.2%
Kepler-29669
 
0.2%
Kepler-3362
 
0.2%
KOI-35162
 
0.2%
Kepler-8062
 
0.2%
Kepler-2660
 
0.2%
Kepler-2060
 
0.2%
Kepler-23558
 
0.2%
Other values (3878)33050
98.0%

Length

2022-11-08T01:18:26.440798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hd1738
 
4.7%
gj288
 
0.8%
a153
 
0.4%
hip106
 
0.3%
epic101
 
0.3%
kepler-1185
 
0.2%
kepler-3276
 
0.2%
kepler-18675
 
0.2%
kepler-29669
 
0.2%
kepler-8062
 
0.2%
Other values (3956)34031
92.5%

Most occurring characters

ValueCountFrequency (%)
e51658
15.8%
-32314
9.9%
K27951
 
8.5%
r26001
 
8.0%
p25835
 
7.9%
l25802
 
7.9%
122967
 
7.0%
214918
 
4.6%
311681
 
3.6%
49682
 
3.0%
Other values (53)78189
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter130461
39.9%
Decimal Number113115
34.6%
Uppercase Letter47982
 
14.7%
Dash Punctuation32314
 
9.9%
Space Separator3065
 
0.9%
Math Symbol44
 
< 0.1%
Other Punctuation17
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K27951
58.3%
H2677
 
5.6%
A2137
 
4.5%
P1901
 
4.0%
D1813
 
3.8%
T1806
 
3.8%
S1582
 
3.3%
L1242
 
2.6%
O1105
 
2.3%
W1072
 
2.2%
Other values (16)4696
 
9.8%
Lowercase Letter
ValueCountFrequency (%)
e51658
39.6%
r26001
19.9%
p25835
19.8%
l25802
19.8%
o309
 
0.2%
a202
 
0.2%
n110
 
0.1%
t107
 
0.1%
c86
 
0.1%
i84
 
0.1%
Other values (12)267
 
0.2%
Decimal Number
ValueCountFrequency (%)
122967
20.3%
214918
13.2%
311681
10.3%
49682
8.6%
59428
8.3%
69324
8.2%
78915
 
7.9%
88879
 
7.8%
08727
 
7.7%
98594
 
7.6%
Other Punctuation
ValueCountFrequency (%)
.15
88.2%
'2
 
11.8%
Dash Punctuation
ValueCountFrequency (%)
-32314
100.0%
Space Separator
ValueCountFrequency (%)
3065
100.0%
Math Symbol
ValueCountFrequency (%)
+44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin178443
54.6%
Common148555
45.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e51658
28.9%
K27951
15.7%
r26001
14.6%
p25835
14.5%
l25802
14.5%
H2677
 
1.5%
A2137
 
1.2%
P1901
 
1.1%
D1813
 
1.0%
T1806
 
1.0%
Other values (38)10862
 
6.1%
Common
ValueCountFrequency (%)
-32314
21.8%
122967
15.5%
214918
10.0%
311681
 
7.9%
49682
 
6.5%
59428
 
6.3%
69324
 
6.3%
78915
 
6.0%
88879
 
6.0%
08727
 
5.9%
Other values (5)11720
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII326998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e51658
15.8%
-32314
9.9%
K27951
 
8.5%
r26001
 
8.0%
p25835
 
7.9%
l25802
 
7.9%
122967
 
7.0%
214918
 
4.6%
311681
 
3.6%
49682
 
3.0%
Other values (53)78189
23.9%

default_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
0
28522 
1
5197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

Length

2022-11-08T01:18:26.553432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:26.681163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

Most occurring characters

ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common33719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII33719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028522
84.6%
15197
 
15.4%

sy_snum
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
1
31267 
2
 
2196
3
 
252
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33719
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

Length

2022-11-08T01:18:26.769177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:27.089463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common33719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131267
92.7%
22196
 
6.5%
3252
 
0.7%
44
 
< 0.1%

sy_pnum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.860375456
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:27.161811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.185062431
Coefficient of variation (CV)0.6370017553
Kurtosis2.312041711
Mean1.860375456
Median Absolute Deviation (MAD)0
Skewness1.538525899
Sum62730
Variance1.404372965
MonotonicityNot monotonic
2022-11-08T01:18:27.257982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
118183
53.9%
27562
22.4%
34421
 
13.1%
42120
 
6.3%
51068
 
3.2%
6277
 
0.8%
862
 
0.2%
726
 
0.1%
ValueCountFrequency (%)
118183
53.9%
27562
22.4%
34421
 
13.1%
42120
 
6.3%
51068
 
3.2%
6277
 
0.8%
726
 
0.1%
862
 
0.2%
ValueCountFrequency (%)
862
 
0.2%
726
 
0.1%
6277
 
0.8%
51068
 
3.2%
42120
 
6.3%
34421
 
13.1%
27562
22.4%
118183
53.9%

discoverymethod
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
Transit
30758 
Radial Velocity
 
2263
Microlensing
 
404
Transit Timing Variations
 
120
Imaging
 
113
Other values (6)
 
61

Length

Max length29
Median length7
Mean length7.690471248
Min length7

Characters and Unicode

Total characters259315
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRadial Velocity
2nd rowRadial Velocity
3rd rowRadial Velocity
4th rowRadial Velocity
5th rowRadial Velocity

Common Values

ValueCountFrequency (%)
Transit30758
91.2%
Radial Velocity2263
 
6.7%
Microlensing404
 
1.2%
Transit Timing Variations120
 
0.4%
Imaging113
 
0.3%
Eclipse Timing Variations24
 
0.1%
Orbital Brightness Modulation20
 
0.1%
Pulsar Timing12
 
< 0.1%
Astrometry2
 
< 0.1%
Pulsation Timing Variations2
 
< 0.1%

Length

2022-11-08T01:18:27.361943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
transit30878
85.0%
radial2263
 
6.2%
velocity2263
 
6.2%
microlensing404
 
1.1%
timing158
 
0.4%
variations146
 
0.4%
imaging113
 
0.3%
eclipse24
 
0.1%
orbital20
 
0.1%
brightness20
 
0.1%
Other values (6)38
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i37022
14.3%
a35864
13.8%
t33354
12.9%
n32146
12.4%
s31510
12.2%
r31484
12.1%
T31036
12.0%
l5008
 
1.9%
o2857
 
1.1%
e2714
 
1.0%
Other values (22)16320
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter220380
85.0%
Uppercase Letter36327
 
14.0%
Space Separator2608
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i37022
16.8%
a35864
16.3%
t33354
15.1%
n32146
14.6%
s31510
14.3%
r31484
14.3%
l5008
 
2.3%
o2857
 
1.3%
e2714
 
1.2%
c2692
 
1.2%
Other values (9)5729
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
T31036
85.4%
V2409
 
6.6%
R2263
 
6.2%
M424
 
1.2%
I113
 
0.3%
E24
 
0.1%
O20
 
0.1%
B20
 
0.1%
P14
 
< 0.1%
A2
 
< 0.1%
Other values (2)2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin256707
99.0%
Common2608
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i37022
14.4%
a35864
14.0%
t33354
13.0%
n32146
12.5%
s31510
12.3%
r31484
12.3%
T31036
12.1%
l5008
 
2.0%
o2857
 
1.1%
e2714
 
1.1%
Other values (21)13712
 
5.3%
Common
ValueCountFrequency (%)
2608
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII259315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i37022
14.3%
a35864
13.8%
t33354
12.9%
n32146
12.4%
s31510
12.2%
r31484
12.1%
T31036
12.0%
l5008
 
1.9%
o2857
 
1.1%
e2714
 
1.0%
Other values (22)16320
6.3%

disc_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.06219
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:27.466094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile2009
Q12014
median2016
Q32016
95-th percentile2021
Maximum2022
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.562915187
Coefficient of variation (CV)0.001768141551
Kurtosis4.729766959
Mean2015.06219
Median Absolute Deviation (MAD)2
Skewness-1.267795543
Sum67945882
Variance12.69436463
MonotonicityNot monotonic
2022-11-08T01:18:27.578118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
201612829
38.0%
20148877
26.3%
20212581
 
7.7%
20121012
 
3.0%
2011969
 
2.9%
2013941
 
2.8%
2018900
 
2.7%
2015848
 
2.5%
2020773
 
2.3%
2010540
 
1.6%
Other values (20)3449
 
10.2%
ValueCountFrequency (%)
19926
 
< 0.1%
19942
 
< 0.1%
19955
 
< 0.1%
199641
 
0.1%
19973
 
< 0.1%
199838
 
0.1%
199975
0.2%
200077
0.2%
200156
0.2%
2002139
0.4%
ValueCountFrequency (%)
2022529
 
1.6%
20212581
 
7.7%
2020773
 
2.3%
2019471
 
1.4%
2018900
 
2.7%
2017494
 
1.5%
201612829
38.0%
2015848
 
2.5%
20148877
26.3%
2013941
 
2.8%

disc_facility
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
Kepler
26073 
K2
 
1857
SuperWASP
 
816
Transiting Exoplanet Survey Satellite (TESS)
 
627
W. M. Keck Observatory
 
600
Other values (61)
3746 

Length

Max length46
Median length6
Mean length7.812034758
Min length2

Characters and Unicode

Total characters263414
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowXinglong Station
2nd rowXinglong Station
3rd rowThueringer Landessternwarte Tautenburg
4th rowThueringer Landessternwarte Tautenburg
5th rowThueringer Landessternwarte Tautenburg

Common Values

ValueCountFrequency (%)
Kepler26073
77.3%
K21857
 
5.5%
SuperWASP816
 
2.4%
Transiting Exoplanet Survey Satellite (TESS)627
 
1.9%
W. M. Keck Observatory600
 
1.8%
La Silla Observatory547
 
1.6%
HATNet475
 
1.4%
Multiple Observatories453
 
1.3%
OGLE273
 
0.8%
HATSouth262
 
0.8%
Other values (56)1736
 
5.1%

Length

2022-11-08T01:18:27.706210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kepler26073
63.6%
observatory1857
 
4.5%
k21857
 
4.5%
superwasp816
 
2.0%
satellite633
 
1.5%
survey629
 
1.5%
transiting627
 
1.5%
exoplanet627
 
1.5%
tess627
 
1.5%
w600
 
1.5%
Other values (107)6629
 
16.2%

Most occurring characters

ValueCountFrequency (%)
e61398
23.3%
r33816
12.8%
l30854
11.7%
K28754
10.9%
p28482
10.8%
7256
 
2.8%
t7006
 
2.7%
a6897
 
2.6%
S5606
 
2.1%
o4585
 
1.7%
Other values (46)48760
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter199722
75.8%
Uppercase Letter51619
 
19.6%
Space Separator7256
 
2.8%
Decimal Number1857
 
0.7%
Other Punctuation1200
 
0.5%
Close Punctuation636
 
0.2%
Open Punctuation636
 
0.2%
Dash Punctuation488
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e61398
30.7%
r33816
16.9%
l30854
15.4%
p28482
14.3%
t7006
 
3.5%
a6897
 
3.5%
o4585
 
2.3%
s4085
 
2.0%
i3949
 
2.0%
v3081
 
1.5%
Other values (16)15569
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
K28754
55.7%
S5606
 
10.9%
O2815
 
5.5%
T2621
 
5.1%
A2166
 
4.2%
E1746
 
3.4%
W1605
 
3.1%
M1393
 
2.7%
P1252
 
2.4%
L1144
 
2.2%
Other values (14)2517
 
4.9%
Space Separator
ValueCountFrequency (%)
7256
100.0%
Decimal Number
ValueCountFrequency (%)
21857
100.0%
Other Punctuation
ValueCountFrequency (%)
.1200
100.0%
Close Punctuation
ValueCountFrequency (%)
)636
100.0%
Open Punctuation
ValueCountFrequency (%)
(636
100.0%
Dash Punctuation
ValueCountFrequency (%)
-488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin251341
95.4%
Common12073
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e61398
24.4%
r33816
13.5%
l30854
12.3%
K28754
11.4%
p28482
11.3%
t7006
 
2.8%
a6897
 
2.7%
S5606
 
2.2%
o4585
 
1.8%
s4085
 
1.6%
Other values (40)39858
15.9%
Common
ValueCountFrequency (%)
7256
60.1%
21857
 
15.4%
.1200
 
9.9%
)636
 
5.3%
(636
 
5.3%
-488
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII263414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e61398
23.3%
r33816
12.8%
l30854
11.7%
K28754
10.9%
p28482
10.8%
7256
 
2.8%
t7006
 
2.7%
a6897
 
2.6%
S5606
 
2.1%
o4585
 
1.7%
Other values (46)48760
18.5%

soltype
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
Published Confirmed
16713 
Kepler Project Candidate (q1_q17_dr25_sup_koi)
2668 
Kepler Project Candidate (q1_q16_koi)
2658 
Kepler Project Candidate (q1_q17_dr25_koi)
2651 
Kepler Project Candidate (q1_q17_dr24_koi)
2639 
Other values (4)
6390 

Length

Max length46
Median length19
Mean length28.77176073
Min length19

Characters and Unicode

Total characters970155
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublished Confirmed
2nd rowPublished Confirmed
3rd rowPublished Confirmed
4th rowPublished Confirmed
5th rowPublished Confirmed

Common Values

ValueCountFrequency (%)
Published Confirmed16713
49.6%
Kepler Project Candidate (q1_q17_dr25_sup_koi)2668
 
7.9%
Kepler Project Candidate (q1_q16_koi)2658
 
7.9%
Kepler Project Candidate (q1_q17_dr25_koi)2651
 
7.9%
Kepler Project Candidate (q1_q17_dr24_koi)2639
 
7.8%
Kepler Project Candidate (q1_q12_koi)2622
 
7.8%
Kepler Project Candidate (q1_q8_koi)2269
 
6.7%
Published Candidate774
 
2.3%
TESS Project Candidate725
 
2.2%

Length

2022-11-08T01:18:27.818183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:27.946131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
published17487
17.6%
candidate17006
17.1%
confirmed16713
16.9%
project16232
16.4%
kepler15507
15.6%
q1_q17_dr25_sup_koi2668
 
2.7%
q1_q16_koi2658
 
2.7%
q1_q17_dr25_koi2651
 
2.7%
q1_q17_dr24_koi2639
 
2.7%
q1_q12_koi2622
 
2.6%
Other values (2)2994
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e98452
 
10.1%
d76170
 
7.9%
i66713
 
6.9%
65458
 
6.7%
r56410
 
5.8%
o48452
 
5.0%
_41640
 
4.3%
a34012
 
3.5%
C33719
 
3.5%
n33719
 
3.5%
Other values (27)415410
42.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter686030
70.7%
Uppercase Letter85845
 
8.8%
Space Separator65458
 
6.7%
Decimal Number60168
 
6.2%
Connector Punctuation41640
 
4.3%
Open Punctuation15507
 
1.6%
Close Punctuation15507
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e98452
14.4%
d76170
11.1%
i66713
 
9.7%
r56410
 
8.2%
o48452
 
7.1%
a34012
 
5.0%
n33719
 
4.9%
t33238
 
4.8%
l32994
 
4.8%
q31014
 
4.5%
Other values (10)174856
25.5%
Decimal Number
ValueCountFrequency (%)
128745
47.8%
210580
 
17.6%
77958
 
13.2%
55319
 
8.8%
62658
 
4.4%
42639
 
4.4%
82269
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C33719
39.3%
P33719
39.3%
K15507
18.1%
S1450
 
1.7%
T725
 
0.8%
E725
 
0.8%
Space Separator
ValueCountFrequency (%)
65458
100.0%
Connector Punctuation
ValueCountFrequency (%)
_41640
100.0%
Open Punctuation
ValueCountFrequency (%)
(15507
100.0%
Close Punctuation
ValueCountFrequency (%)
)15507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin771875
79.6%
Common198280
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e98452
 
12.8%
d76170
 
9.9%
i66713
 
8.6%
r56410
 
7.3%
o48452
 
6.3%
a34012
 
4.4%
C33719
 
4.4%
n33719
 
4.4%
P33719
 
4.4%
t33238
 
4.3%
Other values (16)257271
33.3%
Common
ValueCountFrequency (%)
65458
33.0%
_41640
21.0%
128745
14.5%
(15507
 
7.8%
)15507
 
7.8%
210580
 
5.3%
77958
 
4.0%
55319
 
2.7%
62658
 
1.3%
42639
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII970155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e98452
 
10.1%
d76170
 
7.9%
i66713
 
6.9%
65458
 
6.7%
r56410
 
5.8%
o48452
 
5.0%
_41640
 
4.3%
a34012
 
3.5%
C33719
 
3.5%
n33719
 
3.5%
Other values (27)415410
42.8%

pl_controv_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
0
33659 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

Length

2022-11-08T01:18:28.098277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:28.194296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

Most occurring characters

ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common33719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII33719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033659
99.8%
160
 
0.2%

pl_refname
Categorical

HIGH CARDINALITY

Distinct1861
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
<a refstr=Q1_Q17_DR25_SUPPLEMENTAL_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 Supplemental KOI Table</a>
2668 
<a refstr=Q1_Q16_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q16 KOI Table</a>
2658 
<a refstr=Q1_Q17_DR25_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 KOI Table</a>
2651 
<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR24 KOI Table</a>
2639 
<a refstr=Q1_Q12_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q12 KOI Table</a>
2622 
Other values (1856)
20481 

Length

Max length182
Median length164
Mean length135.9882262
Min length110

Characters and Unicode

Total characters4585387
Distinct characters76
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique924 ?
Unique (%)2.7%

Sample

1st row<a refstr=LIU_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008ApJ...672..553L/abstract target=ref> Liu et al. 2008 </a>
2nd row<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>
3rd row<a refstr=DOLLINGER_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009A&A...505.1311D/abstract target=ref> Dollinger et al. 2009 </a>
4th row<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>
5th row<a refstr=STASSUN_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153..136S/abstract target=ref>Stassun et al. 2017</a>

Common Values

ValueCountFrequency (%)
<a refstr=Q1_Q17_DR25_SUPPLEMENTAL_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 Supplemental KOI Table</a>2668
 
7.9%
<a refstr=Q1_Q16_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q16 KOI Table</a>2658
 
7.9%
<a refstr=Q1_Q17_DR25_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 KOI Table</a>2651
 
7.9%
<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR24 KOI Table</a>2639
 
7.8%
<a refstr=Q1_Q12_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q12 KOI Table</a>2622
 
7.8%
<a refstr=Q1_Q8_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q8 KOI Table</a>2269
 
6.7%
<a refstr=MORTON_ET_AL__2016 href=https://ui.adsabs.harvard.edu/abs/2016ApJ...822...86M/abstract target=ref>Morton et al. 2016</a>2266
 
6.7%
<a refstr=BERGER_ET_AL__2018 href=https://ui.adsabs.harvard.edu/abs/2018ApJ...866...99B/abstract target=ref>Berger et al. 2018</a>2119
 
6.3%
<a refstr=GAJDO_SCARON__ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019RAA....19...41G/abstract target=ref>Gajdo&scaron; et al. 2019</a>1973
 
5.9%
<a refstr=HOLCZER_ET_AL__2016 href=https://ui.adsabs.harvard.edu/abs/2016ApJS..225....9H/abstract target=ref>Holczer et al. 2016</a>1874
 
5.6%
Other values (1851)9980
29.6%

Length

2022-11-08T01:18:28.314289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a35329
 
15.1%
al16537
 
7.1%
et16537
 
7.1%
href=https://exoplanetarchive.ipac.caltech.edu/docs/kepler_koi_docs.html15507
 
6.6%
koi15507
 
6.6%
table</a15507
 
6.6%
target=ref>q1-q177958
 
3.4%
dr255319
 
2.3%
2016</a4953
 
2.1%
2018</a2887
 
1.2%
Other values (4490)97495
41.7%

Most occurring characters

ValueCountFrequency (%)
a332957
 
7.3%
e314128
 
6.9%
t277153
 
6.0%
r266865
 
5.8%
.231174
 
5.0%
199819
 
4.4%
/186085
 
4.1%
s175728
 
3.8%
_165104
 
3.6%
h134560
 
2.9%
Other values (66)2301814
50.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2420066
52.8%
Uppercase Letter669837
 
14.6%
Other Punctuation458205
 
10.0%
Decimal Number419909
 
9.2%
Math Symbol236033
 
5.1%
Space Separator199819
 
4.4%
Connector Punctuation165104
 
3.6%
Dash Punctuation16414
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a332957
13.8%
e314128
13.0%
t277153
11.5%
r266865
11.0%
s175728
 
7.3%
h134560
 
5.6%
c117390
 
4.9%
l104308
 
4.3%
d103464
 
4.3%
f102337
 
4.2%
Other values (16)491176
20.3%
Uppercase Letter
ValueCountFrequency (%)
A67940
10.1%
O66031
9.9%
K64798
9.7%
Q62129
9.3%
T59185
8.8%
E53262
 
8.0%
I53114
 
7.9%
L43273
 
6.5%
R36199
 
5.4%
B25083
 
3.7%
Other values (16)138823
20.7%
Decimal Number
ValueCountFrequency (%)
1116240
27.7%
2101885
24.3%
059790
14.2%
632209
 
7.7%
824262
 
5.8%
723688
 
5.6%
520672
 
4.9%
919798
 
4.7%
416672
 
4.0%
34693
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.231174
50.5%
/186085
40.6%
:33719
 
7.4%
&4188
 
0.9%
;2832
 
0.6%
%190
 
< 0.1%
#12
 
< 0.1%
'5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
=101157
42.9%
>67438
28.6%
<67438
28.6%
Space Separator
ValueCountFrequency (%)
199819
100.0%
Connector Punctuation
ValueCountFrequency (%)
_165104
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3089903
67.4%
Common1495484
32.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a332957
 
10.8%
e314128
 
10.2%
t277153
 
9.0%
r266865
 
8.6%
s175728
 
5.7%
h134560
 
4.4%
c117390
 
3.8%
l104308
 
3.4%
d103464
 
3.3%
f102337
 
3.3%
Other values (42)1161013
37.6%
Common
ValueCountFrequency (%)
.231174
15.5%
199819
13.4%
/186085
12.4%
_165104
11.0%
1116240
7.8%
2101885
6.8%
=101157
6.8%
>67438
 
4.5%
<67438
 
4.5%
059790
 
4.0%
Other values (14)199354
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4585387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a332957
 
7.3%
e314128
 
6.9%
t277153
 
6.0%
r266865
 
5.8%
.231174
 
5.0%
199819
 
4.4%
/186085
 
4.1%
s175728
 
3.8%
_165104
 
3.6%
h134560
 
2.9%
Other values (66)2301814
50.2%

pl_orbper
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct19898
Distinct (%)64.4%
Missing2845
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean13739.64341
Minimum0.09070629
Maximum402000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:28.458275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.09070629
5-th percentile1.625522149
Q14.46976569
median10.55836728
Q327.08246663
95-th percentile226.8904726
Maximum402000000
Range401999999.9
Interquartile range (IQR)22.61270094

Descriptive statistics

Standard deviation2288715.074
Coefficient of variation (CV)166.5774726
Kurtosis30827.71519
Mean13739.64341
Median Absolute Deviation (MAD)7.299555265
Skewness175.5148307
Sum424197750.7
Variance5.238216691 × 1012
MonotonicityNot monotonic
2022-11-08T01:18:28.594544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.28563386
 
< 0.1%
3.578780555
 
< 0.1%
4.437963035
 
< 0.1%
54.42054075
 
< 0.1%
7.794301325
 
< 0.1%
2.54956335
 
< 0.1%
3.5539455
 
< 0.1%
6.246680055
 
< 0.1%
4.95464165
 
< 0.1%
2.684328485
 
< 0.1%
Other values (19888)30823
91.4%
(Missing)2845
 
8.4%
ValueCountFrequency (%)
0.090706291
< 0.1%
0.1797151
< 0.1%
0.1797191
< 0.1%
0.21971
< 0.1%
0.2401041
< 0.1%
0.281
< 0.1%
0.28032261
< 0.1%
0.28032441
< 0.1%
0.32192581
< 0.1%
0.3219621
< 0.1%
ValueCountFrequency (%)
4020000001
< 0.1%
80400001
< 0.1%
73000001
< 0.1%
17900001
< 0.1%
171667.51
< 0.1%
1700001
< 0.1%
166510.171
< 0.1%
83255.091
< 0.1%
77114.071581
< 0.1%
74839.945551
< 0.1%

pl_orbsmax
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct4606
Distinct (%)24.9%
Missing15252
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean2.466653634
Minimum0.0044
Maximum7506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:28.738638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0044
5-th percentile0.026486
Q10.054
median0.0989
Q30.208
95-th percentile2.047
Maximum7506
Range7505.9956
Interquartile range (IQR)0.154

Descriptive statistics

Standard deviation75.10512284
Coefficient of variation (CV)30.44818365
Kurtosis5946.424419
Mean2.466653634
Median Absolute Deviation (MAD)0.0539
Skewness68.85274754
Sum45551.69265
Variance5640.779477
MonotonicityNot monotonic
2022-11-08T01:18:28.874611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0668
 
0.2%
0.05259
 
0.2%
0.04453
 
0.2%
0.0551
 
0.2%
0.07750
 
0.1%
0.05148
 
0.1%
0.06447
 
0.1%
0.03646
 
0.1%
0.06646
 
0.1%
0.04946
 
0.1%
Other values (4596)17953
53.2%
(Missing)15252
45.2%
ValueCountFrequency (%)
0.00441
 
< 0.1%
0.00583
< 0.1%
0.00591
 
< 0.1%
0.005981
 
< 0.1%
0.0062
< 0.1%
0.006221
 
< 0.1%
0.00641
 
< 0.1%
0.00712
< 0.1%
0.007161
 
< 0.1%
0.00761
 
< 0.1%
ValueCountFrequency (%)
75061
< 0.1%
35001
< 0.1%
28801
< 0.1%
25002
< 0.1%
20001
< 0.1%
16621
< 0.1%
11002
< 0.1%
8001
< 0.1%
7731
< 0.1%
7402
< 0.1%

pl_rade
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct4109
Distinct (%)17.6%
Missing10403
Missing (%)30.9%
Infinite0
Infinite (%)0.0%
Mean5.133351089
Minimum0.27
Maximum3791.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:29.018847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.97
Q11.54675
median2.27
Q33.2
95-th percentile13.67
Maximum3791.05
Range3790.78
Interquartile range (IQR)1.65325

Descriptive statistics

Standard deviation65.8764257
Coefficient of variation (CV)12.83302555
Kurtosis2503.599582
Mean5.133351089
Median Absolute Deviation (MAD)0.7845
Skewness49.08722241
Sum119689.214
Variance4339.703463
MonotonicityNot monotonic
2022-11-08T01:18:29.154849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2138
 
0.4%
2.5132
 
0.4%
2.6118
 
0.3%
2.3116
 
0.3%
2.4115
 
0.3%
2.8113
 
0.3%
1.48113
 
0.3%
1.5112
 
0.3%
1.32112
 
0.3%
2.7100
 
0.3%
Other values (4099)22147
65.7%
(Missing)10403
30.9%
ValueCountFrequency (%)
0.272
< 0.1%
0.2771
 
< 0.1%
0.2961
 
< 0.1%
0.3031
 
< 0.1%
0.313
< 0.1%
0.321
 
< 0.1%
0.3541
 
< 0.1%
0.371
 
< 0.1%
0.393
< 0.1%
0.43
< 0.1%
ValueCountFrequency (%)
3791.053
< 0.1%
3163.543
< 0.1%
2783.73
< 0.1%
1226.43
< 0.1%
180.723
< 0.1%
90.811
 
< 0.1%
86.51
 
< 0.1%
78.513
< 0.1%
77.761
 
< 0.1%
77.3421
 
< 0.1%

pl_radj
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1432
Distinct (%)13.7%
Missing23236
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean0.4312446819
Minimum0.025
Maximum8.102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:29.290847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.0941
Q10.149
median0.225
Q30.442
95-th percentile1.38
Maximum8.102
Range8.077
Interquartile range (IQR)0.293

Descriptive statistics

Standard deviation0.4743076012
Coefficient of variation (CV)1.099857276
Kurtosis22.49456468
Mean0.4312446819
Median Absolute Deviation (MAD)0.09
Skewness2.914739432
Sum4520.738
Variance0.2249677006
MonotonicityNot monotonic
2022-11-08T01:18:29.426819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13667
 
0.2%
0.14564
 
0.2%
0.17863
 
0.2%
0.1762
 
0.2%
0.21160
 
0.2%
0.22757
 
0.2%
0.11854
 
0.2%
0.1253
 
0.2%
0.23651
 
0.2%
0.22351
 
0.2%
Other values (1422)9901
29.4%
(Missing)23236
68.9%
ValueCountFrequency (%)
0.0251
 
< 0.1%
0.0261
 
< 0.1%
0.0271
 
< 0.1%
0.0291
 
< 0.1%
0.0321
 
< 0.1%
0.0361
 
< 0.1%
0.0411
 
< 0.1%
0.0422
< 0.1%
0.0442
< 0.1%
0.0454
< 0.1%
ValueCountFrequency (%)
8.1021
< 0.1%
7.7171
< 0.1%
6.91
< 0.1%
6.7051
< 0.1%
6.2291
< 0.1%
4.61
< 0.1%
4.321
< 0.1%
3.61
< 0.1%
3.551
< 0.1%
3.451
< 0.1%

pl_bmasse
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3313
Distinct (%)66.4%
Missing28726
Missing (%)85.2%
Infinite0
Infinite (%)0.0%
Mean736.4316114
Minimum0.015
Maximum25426.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:29.587119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile3.531118
Q123.26516
median251.40353
Q3731
95-th percentile3321.2494
Maximum25426.4
Range25426.385
Interquartile range (IQR)707.73484

Descriptive statistics

Standard deviation1370.384511
Coefficient of variation (CV)1.860844225
Kurtosis37.48372354
Mean736.4316114
Median Absolute Deviation (MAD)241.90353
Skewness4.47421082
Sum3677003.036
Variance1877953.709
MonotonicityNot monotonic
2022-11-08T01:18:29.723428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2120
 
0.1%
413
 
< 0.1%
266.977210
 
< 0.1%
177.98489
 
< 0.1%
15.29
 
< 0.1%
12009
 
< 0.1%
133.48869
 
< 0.1%
8.18
 
< 0.1%
3.98
 
< 0.1%
1.438
 
< 0.1%
Other values (3303)4890
 
14.5%
(Missing)28726
85.2%
ValueCountFrequency (%)
0.0151
< 0.1%
0.021
< 0.1%
0.0661
< 0.1%
0.11
< 0.1%
0.1871
< 0.1%
0.21
< 0.1%
0.290131
< 0.1%
0.2971
< 0.1%
0.3261
< 0.1%
0.3311
< 0.1%
ValueCountFrequency (%)
25426.41
< 0.1%
17668.16971
< 0.1%
13762.0391
< 0.1%
13603.1241
< 0.1%
9852.731
< 0.1%
9534.91
< 0.1%
9501.759531
< 0.1%
9262.628561
< 0.1%
9165.50581
< 0.1%
9057.771
< 0.1%

pl_bmassj
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2526
Distinct (%)50.6%
Missing28727
Missing (%)85.2%
Infinite0
Infinite (%)0.0%
Mean2.31756865
Minimum5 × 10-5
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:29.859487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5 × 10-5
5-th percentile0.0110055
Q10.0735
median0.79295
Q32.3
95-th percentile10.45
Maximum80
Range79.99995
Interquartile range (IQR)2.2265

Descriptive statistics

Standard deviation4.311968381
Coefficient of variation (CV)1.860556916
Kurtosis37.47927393
Mean2.31756865
Median Absolute Deviation (MAD)0.762985
Skewness4.474042775
Sum11569.3027
Variance18.59307131
MonotonicityNot monotonic
2022-11-08T01:18:29.995870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.820
 
0.1%
0.06620
 
0.1%
2.316
 
< 0.1%
216
 
< 0.1%
1.516
 
< 0.1%
0.8415
 
< 0.1%
0.5615
 
< 0.1%
0.4814
 
< 0.1%
0.0214
 
< 0.1%
0.6813
 
< 0.1%
Other values (2516)4833
 
14.3%
(Missing)28727
85.2%
ValueCountFrequency (%)
5 × 10-51
< 0.1%
6 × 10-51
< 0.1%
0.000211
< 0.1%
0.000311
< 0.1%
0.000591
< 0.1%
0.000631
< 0.1%
0.000911
< 0.1%
0.000931
< 0.1%
0.001031
< 0.1%
0.001041
< 0.1%
ValueCountFrequency (%)
801
< 0.1%
55.591
< 0.1%
43.31
< 0.1%
42.81
< 0.1%
311
< 0.1%
301
< 0.1%
29.895881
< 0.1%
29.143491
< 0.1%
28.837911
< 0.1%
28.51
< 0.1%

pl_bmassprov
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing28726
Missing (%)85.2%
Memory size263.6 KiB
Mass
3012 
Msini
1950 
Msin(i)/sin(i)
 
31

Length

Max length14
Median length4
Mean length4.452633687
Min length4

Characters and Unicode

Total characters22232
Distinct characters8
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMsini
2nd rowMsini
3rd rowMsini
4th rowMsini
5th rowMsini

Common Values

ValueCountFrequency (%)
Mass3012
 
8.9%
Msini1950
 
5.8%
Msin(i)/sin(i)31
 
0.1%
(Missing)28726
85.2%

Length

2022-11-08T01:18:30.127916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:30.437641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
mass3012
60.3%
msini1950
39.1%
msin(i)/sin(i31
 
0.6%

Most occurring characters

ValueCountFrequency (%)
s8036
36.1%
M4993
22.5%
i4024
18.1%
a3012
 
13.5%
n2012
 
9.1%
(62
 
0.3%
)62
 
0.3%
/31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17084
76.8%
Uppercase Letter4993
 
22.5%
Open Punctuation62
 
0.3%
Close Punctuation62
 
0.3%
Other Punctuation31
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s8036
47.0%
i4024
23.6%
a3012
 
17.6%
n2012
 
11.8%
Uppercase Letter
ValueCountFrequency (%)
M4993
100.0%
Open Punctuation
ValueCountFrequency (%)
(62
100.0%
Close Punctuation
ValueCountFrequency (%)
)62
100.0%
Other Punctuation
ValueCountFrequency (%)
/31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22077
99.3%
Common155
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s8036
36.4%
M4993
22.6%
i4024
18.2%
a3012
 
13.6%
n2012
 
9.1%
Common
ValueCountFrequency (%)
(62
40.0%
)62
40.0%
/31
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s8036
36.1%
M4993
22.5%
i4024
18.1%
a3012
 
13.5%
n2012
 
9.1%
(62
 
0.3%
)62
 
0.3%
/31
 
0.1%

pl_orbeccen
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct931
Distinct (%)5.5%
Missing16824
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean0.03930700651
Minimum-0.518659
Maximum0.97
Zeros13601
Zeros (%)40.3%
Negative3
Negative (%)< 0.1%
Memory size263.6 KiB
2022-11-08T01:18:30.541643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.518659
5-th percentile0
Q10
median0
Q30
95-th percentile0.28
Maximum0.97
Range1.488659
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1175541312
Coefficient of variation (CV)2.990666084
Kurtosis18.01955118
Mean0.03930700651
Median Absolute Deviation (MAD)0
Skewness3.994901741
Sum664.091875
Variance0.01381897377
MonotonicityNot monotonic
2022-11-08T01:18:30.679230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013601
40.3%
0.1259
 
0.2%
0.0758
 
0.2%
0.0555
 
0.2%
0.1155
 
0.2%
0.0255
 
0.2%
0.0454
 
0.2%
0.0654
 
0.2%
0.0852
 
0.2%
0.0348
 
0.1%
Other values (921)2804
 
8.3%
(Missing)16824
49.9%
ValueCountFrequency (%)
-0.5186591
 
< 0.1%
-0.1292871
 
< 0.1%
-0.0795331
 
< 0.1%
013601
40.3%
0.00011
 
< 0.1%
0.000161
 
< 0.1%
0.00021
 
< 0.1%
0.000641
 
< 0.1%
0.00071
 
< 0.1%
0.0011
 
< 0.1%
ValueCountFrequency (%)
0.971
< 0.1%
0.9561
< 0.1%
0.951
< 0.1%
0.93321
< 0.1%
0.9331
< 0.1%
0.93241
< 0.1%
0.932261
< 0.1%
0.930431
< 0.1%
0.931
< 0.1%
0.9290061
< 0.1%

pl_insol
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct7073
Distinct (%)49.5%
Missing19437
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean359.2447752
Minimum0.02
Maximum58192.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:30.814475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile2.81
Q121.62
median84.56
Q3283.77
95-th percentile1443.27
Maximum58192.75
Range58192.73
Interquartile range (IQR)262.15

Descriptive statistics

Standard deviation1440.4634
Coefficient of variation (CV)4.009698956
Kurtosis782.6551256
Mean359.2447752
Median Absolute Deviation (MAD)75.93
Skewness23.8063538
Sum5130733.88
Variance2074934.808
MonotonicityNot monotonic
2022-11-08T01:18:30.950451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3113
 
< 0.1%
5.5512
 
< 0.1%
7.2911
 
< 0.1%
0.3911
 
< 0.1%
2.1410
 
< 0.1%
8.0810
 
< 0.1%
3.3910
 
< 0.1%
3.7610
 
< 0.1%
2.5110
 
< 0.1%
13.3410
 
< 0.1%
Other values (7063)14175
42.0%
(Missing)19437
57.6%
ValueCountFrequency (%)
0.021
 
< 0.1%
0.062
 
< 0.1%
0.072
 
< 0.1%
0.081
 
< 0.1%
0.14
< 0.1%
0.131
 
< 0.1%
0.143
< 0.1%
0.163
< 0.1%
0.173
< 0.1%
0.185
< 0.1%
ValueCountFrequency (%)
58192.753
< 0.1%
449001
 
< 0.1%
37958.274
< 0.1%
33325.271
 
< 0.1%
25339.91
 
< 0.1%
24240.831
 
< 0.1%
19697.213
< 0.1%
19308.81
 
< 0.1%
14536.991
 
< 0.1%
144291
 
< 0.1%

pl_eqt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1880
Distinct (%)12.3%
Missing18385
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean873.239207
Minimum34
Maximum4050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:31.099735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile344
Q1567
median794
Q31089
95-th percentile1681.35
Maximum4050
Range4016
Interquartile range (IQR)522

Descriptive statistics

Standard deviation421.8185744
Coefficient of variation (CV)0.48305043
Kurtosis2.975137667
Mean873.239207
Median Absolute Deviation (MAD)252
Skewness1.257593066
Sum13390250
Variance177930.9097
MonotonicityNot monotonic
2022-11-08T01:18:31.286755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76741
 
0.1%
58335
 
0.1%
77032
 
0.1%
67431
 
0.1%
61330
 
0.1%
94629
 
0.1%
87529
 
0.1%
50829
 
0.1%
75229
 
0.1%
79328
 
0.1%
Other values (1870)15021
44.5%
(Missing)18385
54.5%
ValueCountFrequency (%)
341
< 0.1%
501
< 0.1%
552
< 0.1%
591
< 0.1%
712
< 0.1%
821
< 0.1%
961
< 0.1%
1031
< 0.1%
1251
< 0.1%
1291
< 0.1%
ValueCountFrequency (%)
40501
 
< 0.1%
39613
< 0.1%
38661
 
< 0.1%
36461
 
< 0.1%
35603
< 0.1%
35591
 
< 0.1%
34461
 
< 0.1%
33701
 
< 0.1%
33531
 
< 0.1%
33201
 
< 0.1%

ttv_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
0
30129 
1
3590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

Length

2022-11-08T01:18:31.443327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:31.566138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

Most occurring characters

ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common33719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII33719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030129
89.4%
13590
 
10.6%

st_refname
Categorical

HIGH CARDINALITY

Distinct1701
Distinct (%)5.1%
Missing313
Missing (%)0.9%
Memory size263.6 KiB
<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
7294 
<a refstr=Q1_Q17_DR25_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 KOI Table</a>
2951 
<a refstr=Q1_Q16_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q16 KOI Table</a>
2656 
<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR24 KOI Table</a>
2638 
<a refstr=Q1_Q12_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q12 KOI Table</a>
2620 
Other values (1696)
15247 

Length

Max length182
Median length164
Mean length129.3101539
Min length110

Characters and Unicode

Total characters4319735
Distinct characters76
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique743 ?
Unique (%)2.2%

Sample

1st row<a refstr=LIU_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008ApJ...672..553L/abstract target=ref> Liu et al. 2008 </a>
2nd row<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>
3rd row<a refstr=DOLLINGER_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009A&A...505.1311D/abstract target=ref> Dollinger et al. 2009 </a>
4th row<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>
5th row<a refstr=STASSUN_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153..136S/abstract target=ref>Stassun et al. 2017</a>

Common Values

ValueCountFrequency (%)
<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>7294
21.6%
<a refstr=Q1_Q17_DR25_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR25 KOI Table</a>2951
8.8%
<a refstr=Q1_Q16_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q16 KOI Table</a>2656
 
7.9%
<a refstr=Q1_Q17_DR24_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q17 DR24 KOI Table</a>2638
 
7.8%
<a refstr=Q1_Q12_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q12 KOI Table</a>2620
 
7.8%
<a refstr=Q1_Q8_KOI_TABLE href=https://exoplanetarchive.ipac.caltech.edu/docs/Kepler_KOI_docs.html target=ref>Q1-Q8 KOI Table</a>2269
 
6.7%
<a refstr=MORTON_ET_AL__2016 href=https://ui.adsabs.harvard.edu/abs/2016ApJ...822...86M/abstract target=ref>Morton et al. 2016</a>2249
 
6.7%
<a refstr=BERGER_ET_AL__2018 href=https://ui.adsabs.harvard.edu/abs/2018ApJ...866...99B/abstract target=ref>Berger et al. 2018</a>2119
 
6.3%
<a refstr=EXOFOP_TESS_TOI href=https://exofop.ipac.caltech.edu/tess/view_toi.php target=ref>ExoFOP-TESS TOI</a>725
 
2.2%
<a refstr=ROWE_ET_AL__2014 href=https://ui.adsabs.harvard.edu/abs/2014ApJ...784...45R/abstract target=ref> Rowe et al. 2014</a>715
 
2.1%
Other values (1691)7170
21.3%

Length

2022-11-08T01:18:31.699909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a35002
16.9%
href=https://exoplanetarchive.ipac.caltech.edu/docs/kepler_koi_docs.html13134
 
6.4%
table</a13134
 
6.4%
koi13134
 
6.4%
et11927
 
5.8%
al11927
 
5.8%
target=ref>ticv8</a7294
 
3.5%
href=https://ui.adsabs.harvard.edu/abs/2019aj....158..138s/abstract7294
 
3.5%
refstr=stassun_et_al__20197294
 
3.5%
target=ref>q1-q175589
 
2.7%
Other values (4116)80999
39.2%

Most occurring characters

ValueCountFrequency (%)
a321039
 
7.4%
e283317
 
6.6%
t264940
 
6.1%
r262246
 
6.1%
.235225
 
5.4%
/186580
 
4.3%
s175922
 
4.1%
173324
 
4.0%
_151972
 
3.5%
h128429
 
3.0%
Other values (66)2136741
49.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2262714
52.4%
Uppercase Letter606914
 
14.0%
Other Punctuation457258
 
10.6%
Decimal Number419666
 
9.7%
Math Symbol233842
 
5.4%
Space Separator173324
 
4.0%
Connector Punctuation151972
 
3.5%
Dash Punctuation14045
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a321039
14.2%
e283317
12.5%
t264940
11.7%
r262246
11.6%
s175922
 
7.8%
h128429
 
5.7%
f101381
 
4.5%
c101257
 
4.5%
d100269
 
4.4%
p83683
 
3.7%
Other values (16)440231
19.5%
Uppercase Letter
ValueCountFrequency (%)
T68721
11.3%
A66284
10.9%
K54509
9.0%
Q52628
8.7%
O52184
8.6%
I50887
8.4%
E45093
 
7.4%
S39623
 
6.5%
L35719
 
5.9%
R24779
 
4.1%
Other values (16)116487
19.2%
Decimal Number
ValueCountFrequency (%)
1118722
28.3%
284429
20.1%
057638
13.7%
845574
 
10.9%
625026
 
6.0%
923655
 
5.6%
520141
 
4.8%
718817
 
4.5%
414066
 
3.4%
311598
 
2.8%
Other Punctuation
ValueCountFrequency (%)
.235225
51.4%
/186580
40.8%
:33406
 
7.3%
&1472
 
0.3%
;357
 
0.1%
%205
 
< 0.1%
#9
 
< 0.1%
'4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
=100218
42.9%
>66812
28.6%
<66812
28.6%
Space Separator
ValueCountFrequency (%)
173324
100.0%
Connector Punctuation
ValueCountFrequency (%)
_151972
100.0%
Dash Punctuation
ValueCountFrequency (%)
-14045
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2869628
66.4%
Common1450107
33.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a321039
 
11.2%
e283317
 
9.9%
t264940
 
9.2%
r262246
 
9.1%
s175922
 
6.1%
h128429
 
4.5%
f101381
 
3.5%
c101257
 
3.5%
d100269
 
3.5%
p83683
 
2.9%
Other values (42)1047145
36.5%
Common
ValueCountFrequency (%)
.235225
16.2%
/186580
12.9%
173324
12.0%
_151972
10.5%
1118722
8.2%
=100218
6.9%
284429
 
5.8%
>66812
 
4.6%
<66812
 
4.6%
057638
 
4.0%
Other values (14)208375
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4319735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a321039
 
7.4%
e283317
 
6.6%
t264940
 
6.1%
r262246
 
6.1%
.235225
 
5.4%
/186580
 
4.3%
s175922
 
4.1%
173324
 
4.0%
_151972
 
3.5%
h128429
 
3.0%
Other values (66)2136741
49.5%

st_spectype
Categorical

HIGH CARDINALITY
MISSING

Distinct225
Distinct (%)11.5%
Missing31764
Missing (%)94.2%
Memory size263.6 KiB
G0 V
 
70
G5 V
 
59
G5
 
59
M0 V
 
56
K0 V
 
53
Other values (220)
1658 

Length

Max length17
Median length14
Mean length3.807161125
Min length1

Characters and Unicode

Total characters7443
Distinct characters42
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)3.6%

Sample

1st rowG8 III
2nd rowK4 III
3rd rowK0 III
4th rowK0 V
5th rowK0 V

Common Values

ValueCountFrequency (%)
G0 V70
 
0.2%
G5 V59
 
0.2%
G559
 
0.2%
M0 V56
 
0.2%
K0 V53
 
0.2%
F8 V51
 
0.2%
G8 V49
 
0.1%
K2 V45
 
0.1%
M1 V43
 
0.1%
G040
 
0.1%
Other values (215)1430
 
4.2%
(Missing)31764
94.2%

Length

2022-11-08T01:18:31.867715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v1098
33.5%
k0135
 
4.1%
g5135
 
4.1%
g0123
 
3.7%
iii94
 
2.9%
f886
 
2.6%
g885
 
2.6%
iv83
 
2.5%
k270
 
2.1%
m068
 
2.1%
Other values (121)1304
39.7%

Most occurring characters

ValueCountFrequency (%)
1326
17.8%
V1268
17.0%
G731
9.8%
K517
 
6.9%
0438
 
5.9%
I433
 
5.8%
M426
 
5.7%
5366
 
4.9%
F275
 
3.7%
2218
 
2.9%
Other values (32)1445
19.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3693
49.6%
Decimal Number2078
27.9%
Space Separator1326
 
17.8%
Other Punctuation274
 
3.7%
Dash Punctuation37
 
0.5%
Lowercase Letter26
 
0.3%
Open Punctuation3
 
< 0.1%
Math Symbol3
 
< 0.1%
Close Punctuation3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V1268
34.3%
G731
19.8%
K517
14.0%
I433
 
11.7%
M426
 
11.5%
F275
 
7.4%
A22
 
0.6%
B10
 
0.3%
D3
 
0.1%
W2
 
0.1%
Other values (5)6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0438
21.1%
5366
17.6%
2218
10.5%
3216
10.4%
1208
10.0%
8191
9.2%
4153
 
7.4%
9109
 
5.2%
692
 
4.4%
787
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
m5
19.2%
e5
19.2%
n4
15.4%
s3
11.5%
u3
11.5%
l3
11.5%
p3
11.5%
Other Punctuation
ValueCountFrequency (%)
.215
78.5%
/51
 
18.6%
&4
 
1.5%
;3
 
1.1%
#1
 
0.4%
Space Separator
ValueCountFrequency (%)
1326
100.0%
Dash Punctuation
ValueCountFrequency (%)
-37
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Math Symbol
ValueCountFrequency (%)
+3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3724
50.0%
Latin3719
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
V1268
34.1%
G731
19.7%
K517
13.9%
I433
 
11.6%
M426
 
11.5%
F275
 
7.4%
A22
 
0.6%
B10
 
0.3%
m5
 
0.1%
e5
 
0.1%
Other values (12)27
 
0.7%
Common
ValueCountFrequency (%)
1326
35.6%
0438
 
11.8%
5366
 
9.8%
2218
 
5.9%
3216
 
5.8%
.215
 
5.8%
1208
 
5.6%
8191
 
5.1%
4153
 
4.1%
9109
 
2.9%
Other values (10)284
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII7443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1326
17.8%
V1268
17.0%
G731
9.8%
K517
 
6.9%
0438
 
5.9%
I433
 
5.8%
M426
 
5.7%
5366
 
4.9%
F275
 
3.7%
2218
 
2.9%
Other values (32)1445
19.4%

st_teff
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4198
Distinct (%)13.4%
Missing2377
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean5474.992311
Minimum575
Maximum57000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:32.004432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum575
5-th percentile3867
Q15110
median5617
Q35955
95-th percentile6354
Maximum57000
Range56425
Interquartile range (IQR)845

Descriptive statistics

Standard deviation1009.645082
Coefficient of variation (CV)0.1844103197
Kurtosis623.1056048
Mean5474.992311
Median Absolute Deviation (MAD)402
Skewness15.50037334
Sum171597209
Variance1019383.193
MonotonicityNot monotonic
2022-11-08T01:18:32.148603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
590082
 
0.2%
560073
 
0.2%
606356
 
0.2%
574153
 
0.2%
582552
 
0.2%
580051
 
0.2%
621051
 
0.2%
618450
 
0.1%
525049
 
0.1%
570049
 
0.1%
Other values (4188)30776
91.3%
(Missing)2377
 
7.0%
ValueCountFrequency (%)
5751
 
< 0.1%
580.51
 
< 0.1%
23201
 
< 0.1%
23751
 
< 0.1%
25502
 
< 0.1%
25597
< 0.1%
25667
< 0.1%
26201
 
< 0.1%
27001
 
< 0.1%
27035
< 0.1%
ValueCountFrequency (%)
570002
< 0.1%
400001
 
< 0.1%
327804
< 0.1%
295643
< 0.1%
293001
 
< 0.1%
277302
< 0.1%
275003
< 0.1%
217001
 
< 0.1%
109001
 
< 0.1%
107001
 
< 0.1%

st_rad
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct553
Distinct (%)1.8%
Missing2257
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1.140893459
Minimum0.01
Maximum83.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:32.316617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.53
Q10.79
median0.95
Q31.2
95-th percentile1.82
Maximum83.8
Range83.79
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation1.671907819
Coefficient of variation (CV)1.465437291
Kurtosis725.3741893
Mean1.140893459
Median Absolute Deviation (MAD)0.19
Skewness23.0423617
Sum35894.79
Variance2.795275757
MonotonicityNot monotonic
2022-11-08T01:18:32.453164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83557
 
1.7%
0.8542
 
1.6%
0.85538
 
1.6%
0.94518
 
1.5%
0.89515
 
1.5%
0.87510
 
1.5%
0.86507
 
1.5%
0.77502
 
1.5%
0.81499
 
1.5%
0.79482
 
1.4%
Other values (543)26292
78.0%
(Missing)2257
 
6.7%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.091
 
< 0.1%
0.115
 
< 0.1%
0.1223
0.1%
0.144
 
< 0.1%
0.158
 
< 0.1%
0.1617
0.1%
0.1728
0.1%
0.188
 
< 0.1%
0.1923
0.1%
ValueCountFrequency (%)
83.81
< 0.1%
71.231
< 0.1%
71.021
< 0.1%
54.921
< 0.1%
51.12
< 0.1%
50.31
< 0.1%
49.71
< 0.1%
492
< 0.1%
47.21
< 0.1%
45.11
< 0.1%

st_mass
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct262
Distinct (%)0.9%
Missing5237
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean0.94592971
Minimum0
Maximum23.56
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:32.589325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q10.82
median0.96
Q31.07
95-th percentile1.31
Maximum23.56
Range23.56
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3199354566
Coefficient of variation (CV)0.3382232878
Kurtosis992.142865
Mean0.94592971
Median Absolute Deviation (MAD)0.12
Skewness17.00348339
Sum26941.97
Variance0.1023586964
MonotonicityNot monotonic
2022-11-08T01:18:32.725126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1805
 
2.4%
1.01690
 
2.0%
0.94670
 
2.0%
1.02668
 
2.0%
0.97668
 
2.0%
0.98665
 
2.0%
0.96656
 
1.9%
1.03654
 
1.9%
0.99646
 
1.9%
0.95621
 
1.8%
Other values (252)21739
64.5%
(Missing)5237
 
15.5%
ValueCountFrequency (%)
01
 
< 0.1%
0.013
 
< 0.1%
0.0210
 
< 0.1%
0.033
 
< 0.1%
0.042
 
< 0.1%
0.054
 
< 0.1%
0.068
 
< 0.1%
0.078
 
< 0.1%
0.0826
0.1%
0.0928
0.1%
ValueCountFrequency (%)
23.561
 
< 0.1%
10.941
 
< 0.1%
10.831
 
< 0.1%
9.11
 
< 0.1%
9.061
 
< 0.1%
8.761
 
< 0.1%
6.731
 
< 0.1%
5.51
 
< 0.1%
4.33
< 0.1%
4.261
 
< 0.1%

st_met
Real number (ℝ)

MISSING
ZEROS

Distinct712
Distinct (%)3.3%
Missing12431
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean-0.03163773018
Minimum-2.5
Maximum0.74
Zeros749
Zeros (%)2.2%
Negative11051
Negative (%)32.8%
Memory size263.6 KiB
2022-11-08T01:18:32.853125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile-0.415
Q1-0.16
median-0.02
Q30.11625
95-th percentile0.31
Maximum0.74
Range3.24
Interquartile range (IQR)0.27625

Descriptive statistics

Standard deviation0.2239456618
Coefficient of variation (CV)-7.078436428
Kurtosis4.237567011
Mean-0.03163773018
Median Absolute Deviation (MAD)0.14
Skewness-0.8673645438
Sum-673.504
Variance0.05015165942
MonotonicityNot monotonic
2022-11-08T01:18:32.981331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0749
 
2.2%
0.07622
 
1.8%
-0.02599
 
1.8%
-0.1580
 
1.7%
-0.14533
 
1.6%
-0.2524
 
1.6%
0.02514
 
1.5%
-0.06514
 
1.5%
-0.08503
 
1.5%
-0.12479
 
1.4%
Other values (702)15671
46.5%
(Missing)12431
36.9%
ValueCountFrequency (%)
-2.53
 
< 0.1%
-1.74
 
< 0.1%
-1.464
 
< 0.1%
-1.443
 
< 0.1%
-1.424
 
< 0.1%
-1.383
 
< 0.1%
-1.084
 
< 0.1%
-16
 
< 0.1%
-0.987
 
< 0.1%
-0.9620
0.1%
ValueCountFrequency (%)
0.741
 
< 0.1%
0.5650
0.1%
0.5452
 
< 0.1%
0.541
 
< 0.1%
0.5221
 
< 0.1%
0.5141
 
< 0.1%
0.513
 
< 0.1%
0.53
 
< 0.1%
0.491
 
< 0.1%
0.4862
0.2%

st_metratio
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing12620
Missing (%)37.4%
Memory size263.6 KiB
[Fe/H]
17211 
[M/H]
3843 
[m/H]
 
38
[Me/H]
 
5
[Fe/H[
 
2

Length

Max length6
Median length6
Mean length5.816057633
Min length5

Characters and Unicode

Total characters122713
Distinct characters8
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[Fe/H]
2nd row[Fe/H]
3rd row[Fe/H]
4th row[Fe/H]
5th row[Fe/H]

Common Values

ValueCountFrequency (%)
[Fe/H]17211
51.0%
[M/H]3843
 
11.4%
[m/H]38
 
0.1%
[Me/H]5
 
< 0.1%
[Fe/H[2
 
< 0.1%
(Missing)12620
37.4%

Length

2022-11-08T01:18:33.109331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T01:18:33.220963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
fe/h17213
81.6%
m/h3881
 
18.4%
me/h5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
[21101
17.2%
/21099
17.2%
H21099
17.2%
]21097
17.2%
e17218
14.0%
F17213
14.0%
M3848
 
3.1%
m38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42160
34.4%
Open Punctuation21101
17.2%
Other Punctuation21099
17.2%
Close Punctuation21097
17.2%
Lowercase Letter17256
14.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H21099
50.0%
F17213
40.8%
M3848
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
e17218
99.8%
m38
 
0.2%
Open Punctuation
ValueCountFrequency (%)
[21101
100.0%
Other Punctuation
ValueCountFrequency (%)
/21099
100.0%
Close Punctuation
ValueCountFrequency (%)
]21097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common63297
51.6%
Latin59416
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
H21099
35.5%
e17218
29.0%
F17213
29.0%
M3848
 
6.5%
m38
 
0.1%
Common
ValueCountFrequency (%)
[21101
33.3%
/21099
33.3%
]21097
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII122713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[21101
17.2%
/21099
17.2%
H21099
17.2%
]21097
17.2%
e17218
14.0%
F17213
14.0%
M3848
 
3.1%
m38
 
< 0.1%

st_logg
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct302
Distinct (%)1.1%
Missing5939
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean4.413044996
Minimum1.1
Maximum7.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:33.357514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile3.98
Q14.32
median4.47
Q34.57
95-th percentile4.73
Maximum7.92
Range6.82
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.2844614632
Coefficient of variation (CV)0.06445922565
Kurtosis22.90030782
Mean4.413044996
Median Absolute Deviation (MAD)0.12
Skewness-3.073739527
Sum122594.39
Variance0.08091832405
MonotonicityNot monotonic
2022-11-08T01:18:33.493495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.54818
 
2.4%
4.5792
 
2.3%
4.49751
 
2.2%
4.47740
 
2.2%
4.56738
 
2.2%
4.51729
 
2.2%
4.55693
 
2.1%
4.58693
 
2.1%
4.57662
 
2.0%
4.52661
 
2.0%
Other values (292)20503
60.8%
(Missing)5939
 
17.6%
ValueCountFrequency (%)
1.11
 
< 0.1%
1.21
 
< 0.1%
1.32
< 0.1%
1.44
< 0.1%
1.442
< 0.1%
1.51
 
< 0.1%
1.512
< 0.1%
1.61
 
< 0.1%
1.622
< 0.1%
1.661
 
< 0.1%
ValueCountFrequency (%)
7.921
 
< 0.1%
5.744
< 0.1%
5.522
 
< 0.1%
5.513
< 0.1%
5.43
< 0.1%
5.353
< 0.1%
5.283
< 0.1%
5.275
< 0.1%
5.247
< 0.1%
5.213
< 0.1%

sy_refname
Categorical

HIGH CARDINALITY

Distinct149
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
33286 
<a refstr=YANG_ET_AL__2022 href=https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.1872Y/abstract target=ref>Yang et al. 2022</a>
 
18
<a refstr=STREET_ET_AL__2016 href=https://ui.adsabs.harvard.edu/abs/2016ApJ...819...93S/abstract target=ref>Street et al. 2016</a>
 
16
<a refstr=KOSHIMOTO_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153....1K/abstract target=ref>Koshimoto et al. 2017</a>
 
12
<a refstr=RYU_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..151R/abstract target=ref>Ryu et al. 2019</a>
 
11
Other values (144)
 
376

Length

Max length160
Median length118
Mean length118.1495893
Min length118

Characters and Unicode

Total characters3983886
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.1%

Sample

1st row<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
2nd row<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
3rd row<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
4th row<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>
5th row<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>

Common Values

ValueCountFrequency (%)
<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>33286
98.7%
<a refstr=YANG_ET_AL__2022 href=https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.1872Y/abstract target=ref>Yang et al. 2022</a>18
 
0.1%
<a refstr=STREET_ET_AL__2016 href=https://ui.adsabs.harvard.edu/abs/2016ApJ...819...93S/abstract target=ref>Street et al. 2016</a>16
 
< 0.1%
<a refstr=KOSHIMOTO_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153....1K/abstract target=ref>Koshimoto et al. 2017</a>12
 
< 0.1%
<a refstr=RYU_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..151R/abstract target=ref>Ryu et al. 2019</a>11
 
< 0.1%
<a refstr=HAN_ET_AL__2020 href=https://ui.adsabs.harvard.edu/abs/2020A&A...642A.110H/abstract target=ref>Han et al. 2020</a>10
 
< 0.1%
<a refstr=HAN_ET_AL__2022 href=https://ui.adsabs.harvard.edu/abs/2022A&A...664A..33H/abstract target=ref>Han et al. 2022</a>9
 
< 0.1%
<a refstr=UDALSKI_ET_AL__2002 href=https://ui.adsabs.harvard.edu/abs/2002AcA....52..115U/abstract target=ref> Udalski et al. 2002 </a>9
 
< 0.1%
<a refstr=SHVARTZVALD_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017ApJ...840L...3S/abstract target=ref>Shvartzvald et al. 2017</a>8
 
< 0.1%
<a refstr=ZANG_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021RAA....21..239Z/abstract target=ref>Zang et al. 2021</a>8
 
< 0.1%
Other values (139)332
 
1.0%

Length

2022-11-08T01:18:33.853873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a33786
24.8%
refstr=stassun_et_al__201933286
24.4%
href=https://ui.adsabs.harvard.edu/abs/2019aj....158..138s/abstract33286
24.4%
target=ref>ticv8</a33286
24.4%
et419
 
0.3%
al419
 
0.3%
target=ref>han86
 
0.1%
target=ref67
 
< 0.1%
2020</a65
 
< 0.1%
2018</a52
 
< 0.1%
Other values (376)1562
 
1.1%

Most occurring characters

ValueCountFrequency (%)
a337959
 
8.5%
.303713
 
7.6%
r269831
 
6.8%
t236616
 
5.9%
s202390
 
5.1%
/202314
 
5.1%
e169154
 
4.2%
_134879
 
3.4%
1134571
 
3.4%
S133442
 
3.3%
Other values (60)1859017
46.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1891781
47.5%
Uppercase Letter572014
 
14.4%
Other Punctuation539804
 
13.5%
Decimal Number506778
 
12.7%
Math Symbol236033
 
5.9%
Connector Punctuation134879
 
3.4%
Space Separator102595
 
2.6%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S133442
23.3%
A101162
17.7%
T100436
17.6%
E33849
 
5.9%
L33803
 
5.9%
J33659
 
5.9%
N33636
 
5.9%
U33446
 
5.8%
I33436
 
5.8%
C33366
 
5.8%
Other values (15)1779
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
a337959
17.9%
r269831
14.3%
t236616
12.5%
s202390
10.7%
e169154
8.9%
d101218
 
5.4%
h101217
 
5.4%
b101161
 
5.3%
f101157
 
5.3%
u67550
 
3.6%
Other values (13)203528
10.8%
Decimal Number
ValueCountFrequency (%)
1134571
26.6%
8100222
19.8%
268710
13.6%
068296
13.5%
966933
13.2%
533650
 
6.6%
333512
 
6.6%
6331
 
0.1%
7306
 
0.1%
4247
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.303713
56.3%
/202314
37.5%
:33719
 
6.2%
&48
 
< 0.1%
%5
 
< 0.1%
;5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
=101157
42.9%
<67438
28.6%
>67438
28.6%
Connector Punctuation
ValueCountFrequency (%)
_134879
100.0%
Space Separator
ValueCountFrequency (%)
102595
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2463795
61.8%
Common1520091
38.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a337959
13.7%
r269831
 
11.0%
t236616
 
9.6%
s202390
 
8.2%
e169154
 
6.9%
S133442
 
5.4%
d101218
 
4.1%
h101217
 
4.1%
A101162
 
4.1%
b101161
 
4.1%
Other values (38)709645
28.8%
Common
ValueCountFrequency (%)
.303713
20.0%
/202314
13.3%
_134879
8.9%
1134571
8.9%
102595
 
6.7%
=101157
 
6.7%
8100222
 
6.6%
268710
 
4.5%
068296
 
4.5%
<67438
 
4.4%
Other values (12)236196
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3983886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a337959
 
8.5%
.303713
 
7.6%
r269831
 
6.8%
t236616
 
5.9%
s202390
 
5.1%
/202314
 
5.1%
e169154
 
4.2%
_134879
 
3.4%
1134571
 
3.4%
S133442
 
3.3%
Other values (60)1859017
46.7%

rastr
Categorical

HIGH CARDINALITY

Distinct3885
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
19h48m27.62s
 
85
19h51m22.15s
 
76
19h54m36.66s
 
75
19h06m09.61s
 
69
19h44m27.02s
 
62
Other values (3880)
33352 

Length

Max length13
Median length12
Mean length11.99911029
Min length11

Characters and Unicode

Total characters404598
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique441 ?
Unique (%)1.3%

Sample

1st row12h20m42.91s
2nd row12h20m42.91s
3rd row15h17m05.90s
4th row15h17m05.90s
5th row15h17m05.90s

Common Values

ValueCountFrequency (%)
19h48m27.62s85
 
0.3%
19h51m22.15s76
 
0.2%
19h54m36.66s75
 
0.2%
19h06m09.61s69
 
0.2%
19h44m27.02s62
 
0.2%
18h57m44.03s62
 
0.2%
19h16m18.61s62
 
0.2%
18h59m45.86s60
 
0.2%
19h10m47.52s60
 
0.2%
19h04m18.98s58
 
0.2%
Other values (3875)33050
98.0%

Length

2022-11-08T01:18:33.965837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19h48m27.62s85
 
0.3%
19h51m22.15s76
 
0.2%
19h54m36.66s75
 
0.2%
19h06m09.61s69
 
0.2%
19h44m27.02s62
 
0.2%
18h57m44.03s62
 
0.2%
19h16m18.61s62
 
0.2%
18h59m45.86s60
 
0.2%
19h10m47.52s60
 
0.2%
19h04m18.98s58
 
0.2%
Other values (3875)33050
98.0%

Most occurring characters

ValueCountFrequency (%)
153738
13.3%
937116
9.2%
h33719
8.3%
m33719
8.3%
.33719
8.3%
s33719
8.3%
228105
 
6.9%
028058
 
6.9%
525682
 
6.3%
425501
 
6.3%
Other values (4)71522
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number269722
66.7%
Lowercase Letter101157
 
25.0%
Other Punctuation33719
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
153738
19.9%
937116
13.8%
228105
10.4%
028058
10.4%
525682
9.5%
425501
9.5%
325430
9.4%
817709
 
6.6%
614335
 
5.3%
714048
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
h33719
33.3%
m33719
33.3%
s33719
33.3%
Other Punctuation
ValueCountFrequency (%)
.33719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common303441
75.0%
Latin101157
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
153738
17.7%
937116
12.2%
.33719
11.1%
228105
9.3%
028058
9.2%
525682
8.5%
425501
8.4%
325430
8.4%
817709
 
5.8%
614335
 
4.7%
Latin
ValueCountFrequency (%)
h33719
33.3%
m33719
33.3%
s33719
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII404598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153738
13.3%
937116
9.2%
h33719
8.3%
m33719
8.3%
.33719
8.3%
s33719
8.3%
228105
 
6.9%
028058
 
6.9%
525682
 
6.3%
425501
 
6.3%
Other values (4)71522
17.7%

ra
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3893
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.1375655
Minimum0.1856063
Maximum359.9749837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:34.092053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1856063
5-th percentile94.1120914
Q1284.2513637
median290.0305195
Q3294.5589885
95-th percentile299.6604865
Maximum359.9749837
Range359.7893774
Interquartile range (IQR)10.3076248

Descriptive statistics

Standard deviation65.72593542
Coefficient of variation (CV)0.246037787
Kurtosis5.261704401
Mean267.1375655
Median Absolute Deviation (MAD)5.013369
Skewness-2.463442039
Sum9007611.57
Variance4319.898587
MonotonicityNot monotonic
2022-11-08T01:18:34.220051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297.115094985
 
0.3%
297.842307376
 
0.2%
298.65273675
 
0.2%
286.540027869
 
0.2%
289.077534862
 
0.2%
284.433464262
 
0.2%
296.112575962
 
0.2%
284.941062760
 
0.2%
287.697991360
 
0.2%
286.079077958
 
0.2%
Other values (3883)33050
98.0%
ValueCountFrequency (%)
0.18560633
< 0.1%
0.3257614
< 0.1%
0.36209956
< 0.1%
0.77448663
< 0.1%
1.04656966
< 0.1%
1.58367717
< 0.1%
2.0729031
 
< 0.1%
2.24929221
 
< 0.1%
2.46944591
 
< 0.1%
2.92763361
 
< 0.1%
ValueCountFrequency (%)
359.97498372
 
< 0.1%
359.90087379
< 0.1%
359.79338512
 
< 0.1%
359.71643022
 
< 0.1%
359.349027713
< 0.1%
359.19151173
 
< 0.1%
359.15197163
 
< 0.1%
358.66887672
 
< 0.1%
358.55884394
 
< 0.1%
358.51513442
 
< 0.1%

decstr
Categorical

HIGH CARDINALITY

Distinct3894
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
+41d54m32.79s
 
85
+46d34m27.69s
 
76
+43d57m17.96s
 
75
+49d26m14.14s
 
69
+39d58m43.48s
 
62
Other values (3889)
33352 

Length

Max length14
Median length13
Mean length12.99053946
Min length10

Characters and Unicode

Total characters438028
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique446 ?
Unique (%)1.3%

Sample

1st row+17d47m35.71s
2nd row+17d47m35.71s
3rd row+71d49m26.19s
4th row+71d49m26.19s
5th row+71d49m26.19s

Common Values

ValueCountFrequency (%)
+41d54m32.79s85
 
0.3%
+46d34m27.69s76
 
0.2%
+43d57m17.96s75
 
0.2%
+49d26m14.14s69
 
0.2%
+39d58m43.48s62
 
0.2%
+46d00m18.61s62
 
0.2%
+49d18m18.45s62
 
0.2%
+46d33m59.22s60
 
0.2%
+42d20m18.88s60
 
0.2%
+39d16m41.65s58
 
0.2%
Other values (3884)33050
98.0%

Length

2022-11-08T01:18:34.364183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41d54m32.79s85
 
0.3%
46d34m27.69s76
 
0.2%
43d57m17.96s75
 
0.2%
49d26m14.14s69
 
0.2%
39d58m43.48s62
 
0.2%
46d00m18.61s62
 
0.2%
49d18m18.45s62
 
0.2%
46d33m59.22s60
 
0.2%
42d20m18.88s60
 
0.2%
39d16m41.65s58
 
0.2%
Other values (3884)33050
98.0%

Most occurring characters

ValueCountFrequency (%)
450292
11.5%
d33719
 
7.7%
m33719
 
7.7%
s33719
 
7.7%
.33698
 
7.7%
331996
 
7.3%
130003
 
6.8%
229841
 
6.8%
+29651
 
6.8%
029576
 
6.8%
Other values (6)101814
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number269454
61.5%
Lowercase Letter101157
 
23.1%
Other Punctuation33698
 
7.7%
Math Symbol29651
 
6.8%
Dash Punctuation4068
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
450292
18.7%
331996
11.9%
130003
11.1%
229841
11.1%
029576
11.0%
529385
10.9%
917820
 
6.6%
817746
 
6.6%
617138
 
6.4%
715657
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
d33719
33.3%
m33719
33.3%
s33719
33.3%
Other Punctuation
ValueCountFrequency (%)
.33698
100.0%
Math Symbol
ValueCountFrequency (%)
+29651
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common336871
76.9%
Latin101157
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
450292
14.9%
.33698
10.0%
331996
9.5%
130003
8.9%
229841
8.9%
+29651
8.8%
029576
8.8%
529385
8.7%
917820
 
5.3%
817746
 
5.3%
Other values (3)36863
10.9%
Latin
ValueCountFrequency (%)
d33719
33.3%
m33719
33.3%
s33719
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII438028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
450292
11.5%
d33719
 
7.7%
m33719
 
7.7%
s33719
 
7.7%
.33698
 
7.7%
331996
 
7.3%
130003
 
6.8%
229841
 
6.8%
+29651
 
6.8%
029576
 
6.8%
Other values (6)101814
23.2%

dec
Real number (ℝ)

HIGH CORRELATION

Distinct3891
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.72690372
Minimum-88.1211107
Maximum85.7365329
Zeros0
Zeros (%)0.0%
Negative4068
Negative (%)12.1%
Memory size263.6 KiB
2022-11-08T01:18:34.484554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-88.1211107
5-th percentile-29.1560639
Q138.8749152
median42.8248275
Q346.6794847
95-th percentile50.0403671
Maximum85.7365329
Range173.8576436
Interquartile range (IQR)7.8045695

Descriptive statistics

Standard deviation25.45722601
Coefficient of variation (CV)0.7548047169
Kurtosis4.326223892
Mean33.72690372
Median Absolute Deviation (MAD)3.8929623
Skewness-2.227241041
Sum1137237.466
Variance648.0703563
MonotonicityNot monotonic
2022-11-08T01:18:34.620558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.909109285
 
0.3%
46.574359576
 
0.2%
43.954988475
 
0.2%
49.437261469
 
0.2%
46.005169662
 
0.2%
49.305123962
 
0.2%
39.978745162
 
0.2%
46.566450560
 
0.2%
42.338578260
 
0.2%
39.27823758
 
0.2%
Other values (3881)33050
98.0%
ValueCountFrequency (%)
-88.12111072
 
< 0.1%
-86.9799652
 
< 0.1%
-84.23174942
 
< 0.1%
-83.74376713
 
< 0.1%
-82.21886932
 
< 0.1%
-82.02749261
 
< 0.1%
-81.7684511
 
< 0.1%
-81.45427561
 
< 0.1%
-80.464604114
< 0.1%
-80.20441692
 
< 0.1%
ValueCountFrequency (%)
85.73653292
< 0.1%
85.23332084
< 0.1%
84.33376132
< 0.1%
83.69753073
< 0.1%
82.24541432
< 0.1%
81.32630963
< 0.1%
81.03898682
< 0.1%
80.13640331
 
< 0.1%
79.78940321
 
< 0.1%
79.25647612
< 0.1%

sy_dist
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3849
Distinct (%)11.7%
Missing793
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean721.6127228
Minimum1.30119
Maximum8800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:34.756743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.30119
5-th percentile43.605075
Q1313.068
median621.855
Q3961.907
95-th percentile1657.36
Maximum8800
Range8798.69881
Interquartile range (IQR)648.839

Descriptive statistics

Standard deviation679.4276629
Coefficient of variation (CV)0.9415405819
Kurtosis35.13923048
Mean721.6127228
Median Absolute Deviation (MAD)318.127
Skewness4.485961484
Sum23759820.51
Variance461621.9492
MonotonicityNot monotonic
2022-11-08T01:18:34.891558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
646.34685
 
0.3%
323.84776
 
0.2%
177.59475
 
0.2%
1209.1662
 
0.2%
369.45162
 
0.2%
848.25462
 
0.2%
335.30760
 
0.2%
282.56360
 
0.2%
428.01858
 
0.2%
107.79657
 
0.2%
Other values (3839)32269
95.7%
(Missing)793
 
2.4%
ValueCountFrequency (%)
1.301192
 
< 0.1%
3.20265
< 0.1%
3.292
 
< 0.1%
3.374541
 
< 0.1%
3.562284
< 0.1%
3.603044
< 0.1%
3.638571
 
< 0.1%
3.672783
 
< 0.1%
3.712079
< 0.1%
3.830782
 
< 0.1%
ValueCountFrequency (%)
88001
 
< 0.1%
86001
 
< 0.1%
82001
 
< 0.1%
81001
 
< 0.1%
77301
 
< 0.1%
77201
 
< 0.1%
77003
< 0.1%
76802
< 0.1%
76002
< 0.1%
75802
< 0.1%

sy_vmag
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2867
Distinct (%)8.6%
Missing422
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean13.72209632
Minimum0.872
Maximum45.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:35.021320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.872
5-th percentile8.25
Q112.803
median14.315
Q315.313
95-th percentile16.154
Maximum45.34
Range44.468
Interquartile range (IQR)2.51

Descriptive statistics

Standard deviation2.364059166
Coefficient of variation (CV)0.172281196
Kurtosis6.076806133
Mean13.72209632
Median Absolute Deviation (MAD)1.163
Skewness-1.144050549
Sum456904.6411
Variance5.588775738
MonotonicityNot monotonic
2022-11-08T01:18:35.157917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.37991
 
0.3%
13.96587
 
0.3%
13.81785
 
0.3%
15.13885
 
0.3%
16.3676
 
0.2%
16.36375
 
0.2%
15.16173
 
0.2%
16.30368
 
0.2%
15.54167
 
0.2%
15.29763
 
0.2%
Other values (2857)32527
96.5%
(Missing)422
 
1.3%
ValueCountFrequency (%)
0.8721
 
< 0.1%
1.125124
< 0.1%
1.995091
 
< 0.1%
2.005381
 
< 0.1%
2.05691
 
< 0.1%
3.230063
< 0.1%
3.296685
< 0.1%
3.312164
< 0.1%
3.352141
 
< 0.1%
3.496074
< 0.1%
ValueCountFrequency (%)
45.341
 
< 0.1%
44.611
 
< 0.1%
43.941
 
< 0.1%
41.621
 
< 0.1%
34.11
 
< 0.1%
32.851
 
< 0.1%
29.31
 
< 0.1%
26.426
< 0.1%
26.11
 
< 0.1%
24.731
 
< 0.1%

sy_kmag
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct3019
Distinct (%)9.1%
Missing443
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean11.71256356
Minimum-3.044
Maximum35.33
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)< 0.1%
Memory size263.6 KiB
2022-11-08T01:18:35.293916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.044
5-th percentile6.3275
Q110.971
median12.365
Q313.206
95-th percentile13.916
Maximum35.33
Range38.374
Interquartile range (IQR)2.235

Descriptive statistics

Standard deviation2.259070459
Coefficient of variation (CV)0.1928758335
Kurtosis4.17456614
Mean11.71256356
Median Absolute Deviation (MAD)0.994
Skewness-1.560801304
Sum389747.2651
Variance5.10339934
MonotonicityNot monotonic
2022-11-08T01:18:35.438248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.38397
 
0.3%
12.82694
 
0.3%
13.63392
 
0.3%
11.96686
 
0.3%
13.51286
 
0.3%
12.1885
 
0.3%
13.51877
 
0.2%
13.34377
 
0.2%
12.75776
 
0.2%
12.62675
 
0.2%
Other values (3009)32431
96.2%
(Missing)443
 
1.3%
ValueCountFrequency (%)
-3.0441
 
< 0.1%
-1.2871
 
< 0.1%
-0.9364
< 0.1%
-0.8061
 
< 0.1%
-0.7831
 
< 0.1%
0.191
 
< 0.1%
0.6715
< 0.1%
0.7793
< 0.1%
1.0383
< 0.1%
1.2254
< 0.1%
ValueCountFrequency (%)
35.331
 
< 0.1%
33.111
 
< 0.1%
31.091
 
< 0.1%
28.291
 
< 0.1%
25.51
 
< 0.1%
24.71
 
< 0.1%
23.932
< 0.1%
23.661
 
< 0.1%
23.43
< 0.1%
23.31
 
< 0.1%

sy_gaiamag
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3612
Distinct (%)10.9%
Missing726
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean13.49266732
Minimum2.92627
Maximum20.1861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size263.6 KiB
2022-11-08T01:18:35.590294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.92627
5-th percentile8.050538
Q112.6373
median14.1237
Q315.0782
95-th percentile15.8265
Maximum20.1861
Range17.25983
Interquartile range (IQR)2.4409

Descriptive statistics

Standard deviation2.295480564
Coefficient of variation (CV)0.1701280044
Kurtosis2.677710801
Mean13.49266732
Median Absolute Deviation (MAD)1.117
Skewness-1.618300635
Sum445163.5728
Variance5.26923102
MonotonicityNot monotonic
2022-11-08T01:18:35.726639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.706285
 
0.3%
15.812376
 
0.2%
14.599475
 
0.2%
15.901569
 
0.2%
13.739762
 
0.2%
14.755562
 
0.2%
13.951362
 
0.2%
15.408960
 
0.2%
12.453560
 
0.2%
15.969558
 
0.2%
Other values (3602)32324
95.9%
(Missing)726
 
2.2%
ValueCountFrequency (%)
2.926273
< 0.1%
2.968285
< 0.1%
3.046281
 
< 0.1%
3.096534
< 0.1%
3.144321
 
< 0.1%
3.248544
< 0.1%
3.272983
< 0.1%
3.414251
 
< 0.1%
3.477195
< 0.1%
3.527691
 
< 0.1%
ValueCountFrequency (%)
20.18611
< 0.1%
19.8791
< 0.1%
18.96681
< 0.1%
18.9122
< 0.1%
18.7651
< 0.1%
18.63332
< 0.1%
18.55771
< 0.1%
17.42291
< 0.1%
17.40762
< 0.1%
17.29341
< 0.1%

rowupdate
Categorical

HIGH CARDINALITY

Distinct362
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Memory size263.6 KiB
2018-09-25
2778 
2014-11-21
2658 
2017-05-08
2651 
2015-08-25
2638 
2014-11-18
2622 
Other values (357)
20371 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters337180
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.1%

Sample

1st row2014-05-14
2nd row2014-07-23
3rd row2018-04-25
4th row2018-04-25
5th row2018-09-04

Common Values

ValueCountFrequency (%)
2018-09-252778
 
8.2%
2014-11-212658
 
7.9%
2017-05-082651
 
7.9%
2015-08-252638
 
7.8%
2014-11-182622
 
7.8%
2013-10-282269
 
6.7%
2018-09-042233
 
6.6%
2016-05-062210
 
6.6%
2019-04-161961
 
5.8%
2016-07-261777
 
5.3%
Other values (352)9921
29.4%

Length

2022-11-08T01:18:35.854639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-09-252778
 
8.2%
2014-11-212658
 
7.9%
2017-05-082651
 
7.9%
2015-08-252638
 
7.8%
2014-11-182622
 
7.8%
2013-10-282269
 
6.7%
2018-09-042233
 
6.6%
2016-05-062210
 
6.6%
2019-04-161961
 
5.8%
2016-07-261777
 
5.3%
Other values (352)9921
29.4%

Most occurring characters

ValueCountFrequency (%)
072185
21.4%
-67436
20.0%
158598
17.4%
256754
16.8%
819409
 
5.8%
517052
 
5.1%
413943
 
4.1%
611815
 
3.5%
910467
 
3.1%
75613
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number269744
80.0%
Dash Punctuation67436
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
072185
26.8%
158598
21.7%
256754
21.0%
819409
 
7.2%
517052
 
6.3%
413943
 
5.2%
611815
 
4.4%
910467
 
3.9%
75613
 
2.1%
33908
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-67436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common337180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
072185
21.4%
-67436
20.0%
158598
17.4%
256754
16.8%
819409
 
5.8%
517052
 
5.1%
413943
 
4.1%
611815
 
3.5%
910467
 
3.1%
75613
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII337180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
072185
21.4%
-67436
20.0%
158598
17.4%
256754
16.8%
819409
 
5.8%
517052
 
5.1%
413943
 
4.1%
611815
 
3.5%
910467
 
3.1%
75613
 
1.7%

pl_pubdate
Categorical

HIGH CARDINALITY

Distinct265
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
2018-08-16
2668 
2014-12-18
2658 
2017-08-31 00:00
2651 
2015-09-24
2639 
2014-12-04
2622 
Other values (260)
20481 

Length

Max length16
Median length7
Mean length8.851389424
Min length7

Characters and Unicode

Total characters298460
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.1%

Sample

1st row2008-01
2nd row2011-08
3rd row2009-10
4th row2011-08
5th row2017-03

Common Values

ValueCountFrequency (%)
2018-08-162668
 
7.9%
2014-12-182658
 
7.9%
2017-08-31 00:002651
 
7.9%
2015-09-242639
 
7.8%
2014-12-042622
 
7.8%
2016-052288
 
6.8%
2014-01-082269
 
6.7%
2018-102141
 
6.3%
2019-032004
 
5.9%
2016-071893
 
5.6%
Other values (255)9886
29.3%

Length

2022-11-08T01:18:35.950613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-162668
 
7.3%
2014-12-182658
 
7.3%
2017-08-312651
 
7.3%
00:002651
 
7.3%
2015-09-242639
 
7.3%
2014-12-042622
 
7.2%
2016-052288
 
6.3%
2014-01-082269
 
6.2%
2018-102141
 
5.9%
2019-032004
 
5.5%
Other values (256)11779
32.4%

Most occurring characters

ValueCountFrequency (%)
077711
26.0%
151874
17.4%
-49226
16.5%
247414
15.9%
816407
 
5.5%
415011
 
5.0%
68302
 
2.8%
77678
 
2.6%
36715
 
2.2%
96510
 
2.2%
Other values (3)11612
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number243932
81.7%
Dash Punctuation49226
 
16.5%
Space Separator2651
 
0.9%
Other Punctuation2651
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
077711
31.9%
151874
21.3%
247414
19.4%
816407
 
6.7%
415011
 
6.2%
68302
 
3.4%
77678
 
3.1%
36715
 
2.8%
96510
 
2.7%
56310
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-49226
100.0%
Space Separator
ValueCountFrequency (%)
2651
100.0%
Other Punctuation
ValueCountFrequency (%)
:2651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common298460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
077711
26.0%
151874
17.4%
-49226
16.5%
247414
15.9%
816407
 
5.5%
415011
 
5.0%
68302
 
2.8%
77678
 
2.6%
36715
 
2.2%
96510
 
2.2%
Other values (3)11612
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII298460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
077711
26.0%
151874
17.4%
-49226
16.5%
247414
15.9%
816407
 
5.5%
415011
 
5.0%
68302
 
2.8%
77678
 
2.6%
36715
 
2.2%
96510
 
2.2%
Other values (3)11612
 
3.9%

releasedate
Categorical

HIGH CARDINALITY

Distinct320
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size263.6 KiB
2018-09-25
2668 
2014-11-21
2658 
2017-05-08
2651 
2015-08-25
2638 
2014-11-18
2622 
Other values (315)
20482 

Length

Max length19
Median length10
Mean length10.00053382
Min length10

Characters and Unicode

Total characters337208
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st row2014-05-14
2nd row2014-07-23
3rd row2014-05-14
4th row2014-07-23
5th row2018-09-06

Common Values

ValueCountFrequency (%)
2018-09-252668
 
7.9%
2014-11-212658
 
7.9%
2017-05-082651
 
7.9%
2015-08-252638
 
7.8%
2014-11-182622
 
7.8%
2018-09-062329
 
6.9%
2013-10-282269
 
6.7%
2016-05-102268
 
6.7%
2019-04-181977
 
5.9%
2016-07-281892
 
5.6%
Other values (310)9747
28.9%

Length

2022-11-08T01:18:36.054612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-09-252668
 
7.9%
2014-11-212658
 
7.9%
2017-05-082651
 
7.9%
2015-08-252638
 
7.8%
2014-11-182622
 
7.8%
2018-09-062329
 
6.9%
2013-10-282269
 
6.7%
2016-05-102268
 
6.7%
2019-04-181977
 
5.9%
2016-07-281892
 
5.6%
Other values (311)9749
28.9%

Most occurring characters

ValueCountFrequency (%)
071575
21.2%
-67438
20.0%
162180
18.4%
255913
16.6%
822329
 
6.6%
517445
 
5.2%
411693
 
3.5%
910242
 
3.0%
68624
 
2.6%
75884
 
1.7%
Other values (3)3885
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number269764
80.0%
Dash Punctuation67438
 
20.0%
Other Punctuation4
 
< 0.1%
Space Separator2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
071575
26.5%
162180
23.0%
255913
20.7%
822329
 
8.3%
517445
 
6.5%
411693
 
4.3%
910242
 
3.8%
68624
 
3.2%
75884
 
2.2%
33879
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-67438
100.0%
Other Punctuation
ValueCountFrequency (%)
:4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common337208
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
071575
21.2%
-67438
20.0%
162180
18.4%
255913
16.6%
822329
 
6.6%
517445
 
5.2%
411693
 
3.5%
910242
 
3.0%
68624
 
2.6%
75884
 
1.7%
Other values (3)3885
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII337208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
071575
21.2%
-67438
20.0%
162180
18.4%
255913
16.6%
822329
 
6.6%
517445
 
5.2%
411693
 
3.5%
910242
 
3.0%
68624
 
2.6%
75884
 
1.7%
Other values (3)3885
 
1.2%

Interactions

2022-11-08T01:18:19.447485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:12.162177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:15.292008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:18.404818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:21.445854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:24.832258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:27.988047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:31.126639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:33.929973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:37.063090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:40.114606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:42.988029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:46.117886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.431134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.281347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.259554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.362462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.246541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.328325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:07.427749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:10.477780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:13.495591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:16.325270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:19.575477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:12.342350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:15.420153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:18.528807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:21.589829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:24.976421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:28.132018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:31.246835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:34.049971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:37.191092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:40.242983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:43.116004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:46.269106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.551135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.409346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.379548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.506353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.367067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.456365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:07.548461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:10.606098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:13.615566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:16.469326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:19.711487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:12.496065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:15.556313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:18.665113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:21.718175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:25.104443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:28.268044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:31.374522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:34.170345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:37.311063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:40.370983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:43.235934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:46.420274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.679322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.529343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.507521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.626734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.495075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.576337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:07.668485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:10.718070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:13.735445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:16.812914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:19.855553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:12.646528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:15.684340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:18.785255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:21.854543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:25.232845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:28.388050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:31.494942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:34.290589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:37.439091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:40.498957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:43.355807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:46.566263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.799579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.649343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.619546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.754688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.615273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.704582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:07.788300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:10.838098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:13.861247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:16.933189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:20.015920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:12.799211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:15.812670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:18.913917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:21.985738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:25.368848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:28.524308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:31.623223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:34.426287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:37.567063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:40.627279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:43.475832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:46.724955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.927578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.769320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.747726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.890582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.751262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.832582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-08T01:17:27.860022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:30.998638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:33.801334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:36.942801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:39.986335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:42.860014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:45.949326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:49.286945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:52.152928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:55.139519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:17:58.026404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:01.117745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:04.199984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:07.083354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:10.350116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:13.375565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:16.189266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T01:18:19.311480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T01:18:36.183086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-08T01:18:36.503888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T01:18:36.775876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T01:18:37.272618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T01:18:37.528868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-08T01:18:37.713366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T01:18:22.975557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T01:18:24.216723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-08T01:18:24.978295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-08T01:18:25.615735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

loc_rowidpl_namehostnamedefault_flagsy_snumsy_pnumdiscoverymethoddisc_yeardisc_facilitysoltypepl_controv_flagpl_refnamepl_orbperpl_orbsmaxpl_radepl_radjpl_bmassepl_bmassjpl_bmassprovpl_orbeccenpl_insolpl_eqtttv_flagst_refnamest_spectypest_teffst_radst_massst_metst_metratiost_loggsy_refnamerastrradecstrdecsy_distsy_vmagsy_kmagsy_gaiamagrowupdatepl_pubdatereleasedate
0111 Com b11 Com121Radial Velocity2007Xinglong StationPublished Confirmed0<a refstr=LIU_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008ApJ...672..553L/abstract target=ref> Liu et al. 2008 </a>326.030001.290NaNNaN6165.6000019.400Msini0.2310NaNNaN0<a refstr=LIU_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008ApJ...672..553L/abstract target=ref> Liu et al. 2008 </a>G8 III4742.0019.002.70-0.350[Fe/H]2.31<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>12h20m42.91s185.178779+17d47m35.71s17.79325293.18464.723072.2824.440382014-05-142008-012014-05-14
1211 Com b11 Com021Radial Velocity2007Xinglong StationPublished Confirmed0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaN1.210NaNNaN5434.7000017.100MsiniNaNNaNNaN0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaNNaNNaN2.60NaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>12h20m42.91s185.178779+17d47m35.71s17.79325293.18464.723072.2824.440382014-07-232011-082014-07-23
2311 UMi b11 UMi011Radial Velocity2009Thueringer Landessternwarte TautenburgPublished Confirmed0<a refstr=DOLLINGER_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009A&A...505.1311D/abstract target=ref> Dollinger et al. 2009 </a>516.220001.540NaNNaN3337.0700010.500Msini0.0800NaNNaN0<a refstr=DOLLINGER_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009A&A...505.1311D/abstract target=ref> Dollinger et al. 2009 </a>K4 III4340.0024.081.800.040[Fe/H]1.60<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>15h17m05.90s229.274595+71d49m26.19s71.823943125.32105.013001.9394.562162018-04-252009-102014-05-14
3411 UMi b11 UMi011Radial Velocity2009Thueringer Landessternwarte TautenburgPublished Confirmed0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaN1.510NaNNaN3432.4000010.800MsiniNaNNaNNaN0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaNNaNNaN1.70NaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>15h17m05.90s229.274595+71d49m26.19s71.823943125.32105.013001.9394.562162018-04-252011-082014-07-23
4511 UMi b11 UMi111Radial Velocity2009Thueringer Landessternwarte TautenburgPublished Confirmed0<a refstr=STASSUN_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153..136S/abstract target=ref>Stassun et al. 2017</a>516.219971.530NaNNaN4684.8142014.740Msini0.0800NaNNaN0<a refstr=STASSUN_ET_AL__2017 href=https://ui.adsabs.harvard.edu/abs/2017AJ....153..136S/abstract target=ref>Stassun et al. 2017</a>NaN4213.0029.792.78-0.020[Fe/H]1.93<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>15h17m05.90s229.274595+71d49m26.19s71.823943125.32105.013001.9394.562162018-09-042017-032018-09-06
5614 And b14 And111Radial Velocity2008Okayama Astrophysical ObservatoryPublished Confirmed0<a refstr=SATO_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008PASJ...60.1317S/abstract target=ref> Sato et al. 2008 </a>185.840000.830NaNNaN1525.500004.800Msini0.0000NaNNaN0<a refstr=SATO_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008PASJ...60.1317S/abstract target=ref> Sato et al. 2008 </a>K0 III4813.0011.002.20-0.240[Fe/H]2.63<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>23h31m17.80s352.824150+39d14m09.01s39.23583775.43925.231332.3314.917812014-05-142008-122014-05-14
6714 And b14 And011Radial Velocity2008Okayama Astrophysical ObservatoryPublished Confirmed0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaN0.680NaNNaN1017.000003.200MsiniNaNNaNNaN0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaNNaNNaN1.20NaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>23h31m17.80s352.824150+39d14m09.01s39.23583775.43925.231332.3314.917812014-07-232011-082014-07-23
7814 Her b14 Her012Radial Velocity2002W. M. Keck ObservatoryPublished Confirmed0<a refstr=NAEF_ET_AL__2004 href=https://ui.adsabs.harvard.edu/abs/2004A&A...414..351N/abstract target=ref> Naef et al. 2004 </a>1796.400002.800NaNNaN1506.450004.740Msini0.3380NaNNaN0<a refstr=NAEF_ET_AL__2004 href=https://ui.adsabs.harvard.edu/abs/2004A&A...414..351N/abstract target=ref> Naef et al. 2004 </a>NaN5255.00NaN0.900.510[Fe/H]4.40<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>16h10m24.50s242.602101+43d48m58.90s43.81636217.93236.619354.7146.383002018-04-252004-012014-08-21
8914 Her b14 Her012Radial Velocity2002W. M. Keck ObservatoryPublished Confirmed0<a refstr=ROSENTHAL_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021ApJS..255....8R/abstract target=ref>Rosenthal et al. 2021</a>1766.410002.830NaNNaN1541.467774.850Msini0.3674NaNNaN0<a refstr=ROSENTHAL_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021ApJS..255....8R/abstract target=ref>Rosenthal et al. 2021</a>NaN5314.941.000.970.405[Fe/H]4.43<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>16h10m24.50s242.602101+43d48m58.90s43.81636217.93236.619354.7146.383002021-09-202021-072021-09-20
91014 Her b14 Her012Radial Velocity2002W. M. Keck ObservatoryPublished Confirmed0<a refstr=GOZDZIEWSKI_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008MNRAS.385..957G/abstract target=ref> Gozdziewski et al. 2008</a>1766.000002.864NaNNaN1581.138004.975Msini0.3590NaNNaN0<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>NaN5280.001.000.910.400[M/H]4.40<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>16h10m24.50s242.602101+43d48m58.90s43.81636217.93236.619354.7146.383002018-04-252008-042014-08-21

Last rows

loc_rowidpl_namehostnamedefault_flagsy_snumsy_pnumdiscoverymethoddisc_yeardisc_facilitysoltypepl_controv_flagpl_refnamepl_orbperpl_orbsmaxpl_radepl_radjpl_bmassepl_bmassjpl_bmassprovpl_orbeccenpl_insolpl_eqtttv_flagst_refnamest_spectypest_teffst_radst_massst_metst_metratiost_loggsy_refnamerastrradecstrdecsy_distsy_vmagsy_kmagsy_gaiamagrowupdatepl_pubdatereleasedate
3370933710ups And cups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=WRIGHT_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009ApJ...693.1084W/abstract target=ref> Wright et al. 2009 </a>241.3300.83200NaNNaN610.233601.920Msini0.2240NaNNaN0<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>NaN6105.511.641.150.101[M/H]4.07<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872015-04-242009-032015-04-24
3371033711ups And dups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=ROSENTHAL_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021arXiv210511583R/abstract target=ref>Rosenthal et al. 2021</a>1282.4102.51700NaNNaN1303.096474.100Msini0.2940NaNNaN0<a refstr=ROSENTHAL_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021arXiv210511583R/abstract target=ref>Rosenthal et al. 2021</a>NaN6156.771.621.290.122[Fe/H]4.13<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872021-09-202021-052021-09-20
3371133712ups And dups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=MCARTHUR_ET_AL__2010 href=https://ui.adsabs.harvard.edu/abs/2010ApJ...715.1203M/abstract target=ref>McArthur et al. 2010</a>1281.5072.53000NaNNaN3257.7411710.250Mass0.3160NaNNaN0<a refstr=MCARTHUR_ET_AL__2010 href=https://ui.adsabs.harvard.edu/abs/2010ApJ...715.1203M/abstract target=ref>McArthur et al. 2010</a>F8 V6089.001.641.310.131[Fe/H]4.25<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872021-08-032010-062021-08-03
3371233713ups And dups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=WRIGHT_ET_AL__2009 href=https://ui.adsabs.harvard.edu/abs/2009ApJ...693.1084W/abstract target=ref> Wright et al. 2009 </a>1278.1002.53000NaNNaN1312.637904.130Msini0.2670NaNNaN0<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>NaN6105.511.641.150.101[M/H]4.07<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872015-04-242009-032015-04-24
3371333714ups And dups And123Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=CURIEL_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011A&A...525A..78C/abstract target=ref> Curiel et al. 2011 </a>1276.4602.51329NaNNaN1313.220004.132Msini0.2987NaNNaN0<a refstr=CURIEL_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011A&A...525A..78C/abstract target=ref> Curiel et al. 2011 </a>F8 VNaN1.561.30NaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872019-01-282011-012019-01-31
3371433715ups And dups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=WITTENMYER_ET_AL__2007 href=https://ui.adsabs.harvard.edu/abs/2007ApJ...654..625W/abstract target=ref> Wittenmyer et al. 2007 </a>1274.6002.51000NaNNaN1255.380003.950Msini0.2420NaNNaN0<a refstr=WITTENMYER_ET_AL__2007 href=https://ui.adsabs.harvard.edu/abs/2007ApJ...654..625W/abstract target=ref> Wittenmyer et al. 2007 </a>F8 VNaNNaNNaNNaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872014-09-182007-012014-09-18
3371533716ups And dups And023Radial Velocity1999Multiple ObservatoriesPublished Confirmed0<a refstr=NAEF_ET_AL__2004 href=https://ui.adsabs.harvard.edu/abs/2004A&A...414..351N/abstract target=ref> Naef et al. 2004 </a>1319.0002.57000NaNNaN1255.380003.950Msini0.2690NaNNaN0<a refstr=NAEF_ET_AL__2004 href=https://ui.adsabs.harvard.edu/abs/2004A&A...414..351N/abstract target=ref> Naef et al. 2004 </a>NaNNaNNaNNaNNaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>01h36m47.60s24.198353+41d24m13.73s41.40381513.40544.095652.8593.986872014-08-212004-012014-08-21
3371633717ups Leo bups Leo111Radial Velocity2021Okayama Astrophysical ObservatoryPublished Confirmed0<a refstr=TENG_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021PASJ..tmp..128T/abstract target=ref>Teng et al. 2021</a>385.2001.18000NaNNaN162.092490.510Msini0.3200NaNNaN0<a refstr=TENG_ET_AL__2021 href=https://ui.adsabs.harvard.edu/abs/2021PASJ..tmp..128T/abstract target=ref>Teng et al. 2021</a>G9 III4836.0011.221.48-0.200[Fe/H]2.46<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>11h36m56.93s174.237219-00d49m24.83s-0.82356452.59734.304902.1844.030402022-01-102021-122022-01-10
3371733718xi Aql bxi Aql011Radial Velocity2007Okayama Astrophysical ObservatoryPublished Confirmed0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaN0.58000NaNNaN642.000002.020MsiniNaNNaNNaN0<a refstr=KUNITOMO_ET_AL__2011 href=https://ui.adsabs.harvard.edu/abs/2011ApJ...737...66K/abstract target=ref> Kunitomo et al. 2011</a>NaNNaNNaN1.40NaNNaNNaN<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>19h54m14.99s298.562449+08d27m39.98s8.46110556.18584.709642.1714.425012014-07-232011-082014-07-23
3371833719xi Aql bxi Aql111Radial Velocity2007Okayama Astrophysical ObservatoryPublished Confirmed0<a refstr=SATO_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008PASJ...60..539S/abstract target=ref> Sato et al. 2008 </a>136.7500.68000NaNNaN890.000002.800Msini0.0000NaNNaN0<a refstr=SATO_ET_AL__2008 href=https://ui.adsabs.harvard.edu/abs/2008PASJ...60..539S/abstract target=ref> Sato et al. 2008 </a>NaN4780.0012.002.20-0.205[Fe/H]2.66<a refstr=STASSUN_ET_AL__2019 href=https://ui.adsabs.harvard.edu/abs/2019AJ....158..138S/abstract target=ref>TICv8</a>19h54m14.99s298.562449+08d27m39.98s8.46110556.18584.709642.1714.425012014-05-142008-062014-05-14